Introduction

Indicator systems are widely used in a context of desertification and are under constant development. DESIRE's rich dataset was used in Research Theme 2 to create an indicator system that calculated the desertification risk index.

The desertification index is a dimensionless value derived for a multivariate analysis over all sites that expresses the degree of desertification, divided into:

• water erosion, on agricultural lands, grass lands/shrub lands and forests
• tillage erosion,
• salinization,
• forest fire,
• water stress,
• overgrazing.

The user selects from a large list of variable the classes that are relevant for the specific situation under investigation, selects the desertification process(es) that are under review. The calculated risk is expressed in a dimensionless value, that has no direct physical meaning but expresses a relative degree of desertification risk that is classified as "no risk" ,"low, "moderate", "high" and "very high" risk.

By calculating the risk for reference sites and for an implemented technology, the assessment tool evaluates if the technology has the intended effect of lowering the risk. There is some interpretation involved to fit a particular situation in the variable classes of the tool. The tool is very versatile, variables that are unknown for a site can be excluded, so it can be adapted to the available data of a site.

The aim of this section of the DESIRE-HIS is to use the experimental results as reported in »Local field experiment results and conclusions in the desertification risk assessment tool, identify bottlenecks and give advice for future development of the tool. However, it is important to realize that the tool is used here out of context: it is not meant to evaluate individual experiments on a field/plot scale as it is based on variables across all sites and also partly based on more regional variables that are not affected at an experimental scale.

Methodology

The methodology is simple: for each site the tool has been used to calculate the desertification index of the reference situation. Subsequently all experiments on a site are included and the change of desertification index is noted. This was repeated for all sites.

Example input and output screens from one of the Guadalentín study site experiments

The change in desertification risk is then compared against the overall results. However, this is not a direct comparison, because this is not the intended purpose of the tool. For instance water stress risk can occur on a site because of a range of circumstances related to climate, soil, vegetation, farming practices and so on. A direct comparison against a measured soil moisture change on a certain depth in the soil is useless because many more factors are involved. So a lowering of water stress risk because of a technology and a change in moisture content cannot be compared, even relatively. The same holds true for all other processes/risks. Therefore the results are compared in a more general sense.

Conclusions and recommendations

1. Overall the results are good, the tool shows what it is supposed to show: a lowering of a risk coincides with the application of a technology, in almost all cases. Since the risk factor is based on multivariate statistics, both the calculated risk, and the change of risk after application of a technology is relatively smaller than the experimental results often indicate. This is logical because the tool is an average of all sites and the effect of a particular situation will then be less pronounced.
2. The results indicate that overall there are advantages and disadvantages in the tool. The advantages are first you get a single value for a desertification risk with which you can compare all sites, so very different processes become comparable, and second, if so desired, you get information on desertification risks that were not initially researched at a site. For instance an experiment that is meant to control water erosion also may have a certain risk for tillage erosion or water stress although this was not the subject of the experiment. Thus you get more information and a wider context.
3. In the future, the tool could focus more on conservation and mitigation measures, this would make valuable addition, and permit much more than calculating a risk. This is logical: the tool was based on many observation points in each site, but since most of these sites have no widescale implementation of conservation measures, conservation is not part of the multivariate analysis. Conservation measures that are included are: conservation tillage types, a decrease of slope (terracing) and an increase in soil cover/mulch. At this point many technologies have to broken down in the processes they aim to affect but there is some interpretation needed for this. Contour ploughing could be interpreted as runoff control, intercropping as an increase of cover, drip irrigation as a decrease of overexploitation of groundwater, terraces must be combined with a decrease in sloep and runoff control before it has effect. At this point there is some freedom to make the system do what you want and like all models background knowledge is needed to operate it correctly.
4. Some variables are really regional variables that do not change as a result of a technology (degree of land abandonment, climate, deforestation degree etc), and are therefore not distinctive between a traditional way of farming and an experiment. If these have a large effect in the end result they ‘overshadow’ the variables that are related directly to an experiment.
5. Some relations are counterintuitive and show that this is a statistical tool and not a process based tool (although the variables link to processes). The most important are: rainfall amount is not a factor influencing water stress risk, only seasonality is. You can generate water stress in an areas with > 1500 mm rainfall, or no water stress in an area with < 280 mm of rainfall. Neither slope nor soil cover seems to affect water erosion very much, when many other factors are involved. Implementing terraces with a slope of <2% has little effect, as does implementing a full surface cover.  Also processes are not linked: for example the effect of terraces in China is an increased water availability because of the increased infiltration on the flat surface as opposed to the sloping surface, but this relation is not included in the statistical basis of the indicator risk assessment tool and will therefore not show up. So terraces do not alleviate water stress.
6. The tool generally does what it is supposed to do and the risk factors reflect well the experiments as reported in »Local field experiment results and conclusions. Some experience and knowledge is needed to operate the tool correctly. A clear advice for a future extension of the indicator tool is to i) extend the dataset with a focus on conservation measures, and ii) show the relative importance of the many variables so that it is clearer which variable has the largest influence on the result. This helps in understanding how the desertification index is generated and how large the effect of a technology must be to generate a difference, a decrease in risk, iii) look at counterintuitive relations that are apparent and somewhat confusing.

More details ... download the full report
	Evaluating the desertification indicator system with experimental results (Report 98 D4.4.1 May12) [1.69Mb]

Institute full name:

Agricultural University of Athens, Department of Natural Resources Development and Agricultural Engineering (NRDAE)

Institute acronym:

AUA

Institute profile:

AUA is the third oldest University in Greece and the first devoted to Agriculture. The Department of NRDAE (one of the 7 departments of the AUA) has three sectors. The participating Sectors of: Water Resources Management and of  Soils and Agricultural Chemistry have an over 50 years experience in successful international research including: water resources management, soil physics, surface and groundwater hydrology, water treatment and reuse, erosion, desertification, flood and drought management, natural resources management, policy analysis, field investigations of soil erosion parameters and impacts on land productivity, land evaluation and sustainable development, etc. Project team members have participated in the compilation of the «Greek National Action Plan for Combating Desertification» and in several major projects such as CORINE, WASTES, MEDALUS I, II, III, PESERA, DESERTLINKS, ARID, WSM, etc. AUA is coordinator of Research Theme 2 of the DESIRE project.

Website

www2.aua.gr

Address

Agricultural University of Athens
Iera Odos 75, 11855 Athens, Greece
Fax. +30210-5294081

Institute logo

Institute image

Involved personnel

Name	Contact details	Key qualifications	Photo
Prof. Christos A. Karavitis	Tel: +30210 529 4073 E-mail: ckaravitis@aua.gr	Agric. & Civil Eng. Areas of specialization include Integrated Water Resources Management, Decision Support Systems in Natural Resources, Droughts, Flood and Risk Management, Indicators Methodology etc. He has participated as research leader in many research projects both in Europe and in USA.
Prof. Constantinos Kosmas	Tel: +30210 529 4097 E-mail: lsos2kok@aua.gr	He is a specialist in land resources, desertification, applied pedology and agricultural ecosystems. He has participated as research leader or project coordinator in many international research projects. He is the President of the Greek committee for combating desertification.
Prof. Nikolaos Moustakas	Tel: +30210 529 4099 E-mail: nmoustakas@aua.gr	Prof. in soils. His experience is focused on soil genesis and soil degradation especially in contamination by heavy metals and nitrates and desertification. He has involved in various research projects of land degradation and desertification.
Prof. Evan C. Vlachos	Tel: +1 970 491 6089 E-mail: evlachos@engr.colostate.edu	Prof. of Sociology and Civil and Environmental Eng. and Assoc. Director Intern. School for Water Resources, Colorado State University, USA. He has consulted with the US Corps of Engineers, NATO, Unesco, and in a variety of countries, esp. in the Mediterranean and Middle East. His expertise is in IWRM, Droughts, Transboundary water problems, Public Participation, and Environmental Security
Aikaterine Kounalaki (M.Sc.)		Expert in Public Relations and Media Communication. She is experienced in the design and implementation of training, educational, research and consulting programs in the field of public management.

The methodology for defining land degradation and Desertification Risk Index (DRI) that is used by the Desertification Risk Assessment Tool was initially assessed using independent data on erosion and organic matter content of the soil, that were collected in the study sites of Greece. Since the collected data for the various indicators were drawn only from one country (Greece) they offer no significant variations especially in the indicators related to socio-economic characteristics.

The DESIRE methodology was developed using data from a wide range of physical environment and socio-economic characteristics from all over the world. However, the indicator data collected could not be used for the application phase, since they were already used for the DRI development. Thus, in order to avoid autocorrelation, independent data had to be used.

Therefore, the DRI calculation was performed using independent data produced by indicator values estimated for each case study area, based on the WOCAT QM database also developed in the DESIRE project (see »WOCAT tool for mapping land degradation and land conservation). The following study sites representing different desertification processes were chosen for this purpose: Sehoul Morocco (water erosion), Eskisehir Turkey (water erosion), Dzhanibek Russia (Overgrazing, Salinization, water stress), Kelaigou China (tillage practices resulting in increased water erosion) and Mação Portugal (forest fires, water erosion).

From the land use system, land degradation and sustainable land management information described in the database for each map unit, the pertinent data was translated into indicators values following the assigned indicator categories and weights according to the risk assessment method. Then, the indicator values were inserted in the Desertification Risk Assessment Tool. Overall, 1119 indicators in 83 map units in the five case study areas were assigned, whose values were calculated. Following this procedure the DRI was estimated for every map unit. In the case that there were more than one degradation processes per map unit, then the dominant process was used as it was described in the WOCAT QM database. Finally, the DRI values for each map unit were depicted in a color scale and the results were visualized in the corresponding maps.

Map 1: Desertification risk index (DRI), Yan River Basin study site	Map 2: Degree of land degradation (as assessed using the WOCAT mapping method), Yan River Basin study site

The Assessment Tool was also applied to all the other DESIRE study sites, although the results are not included in the HIS. Overall, the application of the Desertification Risk Assessment Tool has produced output that generally correlates with the WOCAT approach. The indicator values derived from the WOCAT QM database have given results that were very similar to those of the WOCAT maps. Although the same database was used in both approaches, the methods that were applied were quite different. The DRI approach was developed based on the collected datasets under specific protocols and uses indices to estimate desertification risk, while the WOCAT QM method provides maps of degradation status (including existing trends) based on protocols of expert assessment. Despite of such differences in the approaches, the resulting maps were comparable. This demonstrates the complementarity of both approaches, as well as their distinctiveness in usage. Furthermore, the Desertification Risk Assessment Tool seems robust enough to provide reasonable approximations not only when quantitative datasets (such as the indicator database) are available, but also when such data are not in abundance and estimates and translations from existing data sets have to be used to provide the necessary input. Since the creation of a full quantitative dataset of indicators is a labour-intensive task, it is an advantage that the method may also be used with less elaborate databases. This facilitates the application of Assessment Tool in other areas, and thus allows land use managers around the world to initially estimate what the effect of using different management options might be on desertification risk.

Conclusions

The DESIRE indicator based methodology for an expert system development was achieved using data from a wide range of physical environment and socio-economic situations, at a world-wide scale. The application results obtained have shown that indicators may be used in assessing land degradation and desertification risk for a wide range of physical environmental, social and economic conditions in the study sites. The application in areas beyond the Mediterranean region may constitute an innovation per se. Additionally, the use of two different data bases worldwide contributes also to the originality of the approach. In order to apply the methodology, it is important to have data for all the indicators assigned for each process or cause of land degradation. However, should the data be incomplete, existing similar data may be translated into indicators and then used.

The comparative analysis has shown that indicators may be used worldwide for assessing desertification risk. The methodology developed here may be used to assess the efficiency and efficacy of different land management practices and degradation monitoring techniques for combating desertification at farm level and, given the required information, at regional level, in a variety of locales. The system of indicators may enable land users to test different scenarios for ecosystem vulnerability in order to assess critical stress factors and their impacts on desertification. In this regard, the assessment tool may allow decision-makers worldwide to generate appropriate and timely desertification measures by estimating how the desertification risk changes through the application of certain applied responses. It may also provide a standard for assessing the effectiveness of the various land management practices. For such an assessment, the desertification risk assessment method has the following advantages:

• presentation and evaluation of a variety of desertification indicators simultaneously;
• delineation of the desertification risk (results) in a concise and holistic fashion;
• direct association of data input to the sensitivity of the results;
• interdisciplinary criteria and evaluation process; and
• integration among experts, administrators and decision-makers, since input from each group is needed for a successful run of the algorithm.

Finally, around the world successful development of expert (and similar decision support) systems and application of desertification mitigation and general natural resources management calls for flexible adaptive institutions of a scale and character that will enable them to be utilized effectively in a fast changing, evolving and complex socio-economic environment. The results of this work should be imbedded in a policy options framework incorporating a structural participatory process involving stakeholders, experts, end users and decision makers.

»Agricultural University of Athens (GR)

Christos A. Karavitis, Constantinos Kosmas, Vassilia Fassouli, Nikolaos Moustakas, Evan C. Vlachos, Aikaterine Kounalaki

... and all the study site teams

»Estación Experimental de Zonas Áridas (ES)	»Universidade de Aveiro (PT)
»Escola Superior Agrária de Coimbra (PT)	»University of Wales Swansea
»Agricultural University of Athens (GR)	»Democritus University of Thrace (GR)
»Eskişehir Osmangazi University (TR)	»UNESCO-GN Chair, University Mohammed V-Agdal, Rabat (MA)
»Institut des Régions Arides (TN)	»Moscow State University of Environmental Engineering (RU)
»Institute of Soil and Water Conservation, Chinese Academy of Sciences (CN)	»University of Botswana (BW)
»Institut de Recherche pour le Développement (FR)	»Instituto de Investigaciones Agropecuarias (CL)
»Instituto Nacional de Investigação e Desenvolvimento Agrário (CV)

European Journal of Soil Science, June 2009, 60, 412–419

Soils are commonly stony, especially in steep upland or heavily degraded sites. The hydrological effect of large stone contents has been previously investigated in wettable but not in water-repellent soils. For the latter, the focus has instead been on the impact of other soil characteristics (e.g. cracks and macropores) likely to promote water percolation. This paper investigates stone effects on water flow in water-repellent sand under laboratory conditions. Seventy-five experiments were performed on a water-repellent sand mixed with a range of quantities of different-sized wettable and water-repellent stones. The time taken for water to pass through each sand–stone mix, the percolated water volumes and numbers of dry and wet stones following each 60-minute experiment were recorded. At large stone contents (> 55% or > 65% by weight, depending on stone wettability), percolation occurred relatively quickly and in comparatively large quantities. At intermediate stone contents (45–65%) percolation response was variable and at stone contents < 45% for wettable and < 55% for water-repellent soils no water percolation occurred. We argue that with large stone contents flow pathways develop along sand–stone interfaces and a continuous preferential flow path can form provided there are sufficient stone-to-stone connections. The distribution and alignment of the stones, especially at intermediate stone contents, are important for promoting water movement. Water repellency determinations based only on the fine sediment component in stony soils could therefore be misleading as regards determining their hydrological response: the influence of the clastic component must also be considered.

© 2009 The Authors

In attempts to mitigate desertification, scientists have paid a lot of attention to ‘on site’ erosion processes at the hillslope scale. Generally, a large part of the eroded sediment is redeposited again before it reaches a river. Therefore, it is generally expected that the annual amount of sediment that reaches a catchment outlet (i.e. the ‘sediment yield’ of a river) is smaller than the total annual amount of on-site erosion that occurs in the catchment. As a result, catchment sediment yield has received relatively little attention in studies that assess the causes and impacts of desertification.

Analyses of large databases of measured hillslope erosion rates and catchment sediment yields indicated that this is generally not true for the Mediterranean region where catchment sediment yields are generally much higher than would be expected from the on-site erosion rates. Soils on Mediterranean hillslopes are often very shallow and stony, making them relatively less susceptible to erosion. However, the water that runs off from these hillslopes can cause serious erosion problems further downstream due to other processes such as gully erosion, landslides and riverbank erosion.

© M. Vanmaercke (Marmora, Morocco) October 2009

High catchment sediment yields can cause various problems. One of the most important is the storage capacity loss of reservoirs, since large volumes of sediments are often trapped in reservoirs. Hence, high catchment sediment yields can threaten future water supplies. Sediment yield is also closely linked with various environmental/ecological issues and can lead to higher flooding risks. Although the importance of these other erosion processes differs from catchment to catchment, these results clearly indicate that not only on-site erosion but also catchment sediment yield should be considered as an important desertification indicator.

The research conducted by K.U.Leuven and other partners of the DESIRE project therefore clearly indicated that it is both necessary and feasible to include catchment sediment yield in desertification assessment studies. Details on how sediment yield can be used as a desertification risk indicator are given in the references below.

• the analysis of a wide range of alternatives for land management practices;
• the evaluation and selection of the main indicators through which desertification risk may be assessed in a variety of locales worldwide;
• the development of a consensus among various groups (politicians, managers, experts, etc.) in assessing desertification risks and thus, the Tool becomes the arena of focused disagreement.

In this regard, the Tool may allow the decision-makers to generate appropriate and timely desertification measures. It may also provide a standard for assessing the effectiveness of the various land management practices. The program is designed to run in a Windows environment. An effort was made that the program would be user friendly and self-explanatory, guiding the user step by step.

More details ... download the Desertification Risk Assessment Tool and accompanying documentation
	WP2.2 Desertification Risk Assessment Tool software (v5g) WP2.2 Desertification Risk Assessment Tool library files (v5d) WP2.2 Desertification Risk Assessment Tool documentation (v5g) WP2.2 Desertification Risk Assessment Tool install instructions (v1b)

Combating desertification in Mediterranean Europe: linking science with stakeholders Funded by European Commission contract number: EVK2-CT2001-00109. 2001-2004

DIS4ME gives access to some 148 indicators of relevance to Mediterranean desertification. It was developed by the DESERTLINKS EU-funded research project to provide a tool to enable users from a wide range of backgrounds, including scientists, policy makers and farmers, to:

• identify where desertification is a problem;
• assess how critical the problem is;
• better understand the processes of desertification.

DIS4ME contains a variety of information and calculation tools including:

• a database of indicators relevant to Mediterranean desertification
• methods for using and combining indicators
• using indicators to link science to stakeholders.

DIS4ME is translated into English, Spanish, Portuguese, Italian, and Greek.