IRENA Indicator Fact Sheet IRENA indicator 29 Soil quality

Transcription

1 Indicator Definition Topsoil (0-30cm) organic carbon content Input Indicator links: IRENA 24 - Land cover change Output Indicator Links: IRENA 23 - Soil erosion Key message Soil quality cannot be measured directly. For the EU, soil organic carbon content in topsoil has been defined as the more appropriate indicator for soil quality. High organic carbon content corresponds to good soil conditions from an agro-environmental point of view: limited soil erosion, high buffering and filtration capacity, rich habitat for soil organisms, enhanced sink for atmospheric carbon dioxide, etc.. Soils with Organic Carbon content between 1 and 10 % can also be considered of high agricultural value, while soils with less the 1% can be considered as affected by severe degradation (desertification). Peat and peaty soils should be considered separately in this context. Figure 29.1 Estimated organic carbon content (%) in the surface horizon (0-30 cm) of soils in Europe Source: Joint Research Centre,

2 Results and assessment Introduction SOIL QUALITY is how well soil does what we want it to do. More specifically, soil quality is the capacity of a specific kind of soil to function, within natural or managed ecosystem boundaries, to sustain plant and animal productivity, maintain or enhance water and air quality, and support human health and habitation (USDA, A definition of soil quality for the purposes of the EU is lacking. Nevertheless the Commission has put forward a number of criteria (COM 179 (2002)) for defining well functioning soils from the European perspective. Four out of five functions are directly linked to soil organic carbon content: food and biomass production, filtering and buffering capacity, pool of biodiversity and source of raw materials (peat). Therefore the levels of soil organic carbon in topsoil have been adopted as the more appropriate indicator for soil quality for agro-environmental purposes, since it covers both strictly agricultural criteria and wider environmental concerns. A new important aspect related to soil organic carbon is the recognition of the importance of the soil organic carbon pool in the context of the European Climate Change Programme ( Policy relevance and context The decline of soil organic carbon content has been identified by the Commission in COM 179 (2002) Towards a EU Thematic Strategy for Soil protection as one of the priority areas where immediate action is needed. The importance of organic carbon was as well recognised by the Council and the European Parliament in their position papers following the above communication. Council Regulation (EC) No. 1782/2003 of 29 September 2003 has set up the crosscompliance as compulsory measure from Farmers receiving direct payments must respect statutory standards in the fields of environment, food safety, and animal health and welfare at farm level. The beneficiaries of direct payments will also be obliged to maintain their agricultural land in good agricultural and environmental condition, in order to avoid deterioration of land and land abandonment. They will also have to maintain the share of land under permanent pasture. The main issues that Member States should addressed in the Good Agricultural and Environmental conditions are: soil erosion, decline of soil organic matter and physical soil degradation. This indicator addresses the second issue, the decline of soil organic carbon content. It will be necessary to further develop this indicator with a time component (soil monitoring) in order to detect impacts of soil protection measures. The need for a comprehensive approach for soil monitoring has been well recognised by the Commission in its communication and will be one of the main elements of the forthcoming EU Thematic Strategy for Soil Protection. Monitoring changes in Soil Organic Carbon content will be particularly relevant in the context of potential contribution of soils to the achievement of the Kyoto target. A mitigation potential of MtCO2-eq has been estimated (ECCP, 2003) provided by conservation and enhancement of carbon stocks by extensification (e.g. organic farming, conservation agriculture) and other Good Agricultural Practices. Agri-environmental Context The original definition of the soil quality indicator, as for example in COM(2001)144, was strictly linked to a mono-functional perspective on soil protection. COM (2002) 179 has clarified that in the EU soils are considered in their full multi-functionality. Therefore, soil quality, as previously defined, was not taking into account this new European perspective. Meanwhile also in other OECD countries (USDA, D.L. Karlen et al., Geoderma 114 (2003) ) a fundamental re-thinking of the soil quality concept is taking place. The close link between soil organic carbon and a number of related soil properties make it one of the best candidates as a newly defined soil quality indicator for Europe. Assessment Fig displays the data layer of continuous Soil Organic Carbon values grouped into 7 classes. The distribution has been validated against a number of National assessments, with good accordance. Areas in southern Europe with OC_TOP between 0 1% appear in the 2

3 expected places and correspond with areas with high soil erosion rates (see indicator 23). The organic soils (peat) in northern Europe are clearly highlighted. An estimated distribution of major classes of Organic Carbon in topsoil in Europe shows that 45% of the area covered by this assessment corresponds to soils with medium organic carbon content (good condition). Soils with low and very low organic carbon content account for ca. 45% and the rest is to be considered as organic soils (peat and peaty soils). A clear humidity and temperature trend is present, with soils in the dry and warm Mediterranean area suffering of low to very low organic carbon content. This corresponds to areas that have been recognised in other assessments related to indicators for desertification in the Mediterranean ( as areas highly sensitive to desertification processes. Of particular importance for assessing the potential of soils for mitigation strategies towards the Kyoto target are the aggregated results at National level (fig. 29.2) allowing a good estimate of the current carbon pools in European soils. Main single factor determining soil organic carbon content is land use. New land cover (CLC2000) and land use (LUCAS) data will allow monitoring soil organic carbon changes over time. 3

4 References IRENA Indicator Fact Sheet Arrouays, D., Deslais, W. and Badeau, V. (2001). The carbon content of topsoil and its geographical distribution in France. Soil Use and Management 17, Batjes, N.H. (1996). Total carbon and nitrogen in the soils of the world. European Journal of Soil Science 47, Bullock, P., Jones, R.J.A. and Montanarella, L. eds. (1999). (eds). Soil Resources of Europe. European Soil Bureau Research Report No.6, EUR EN, (1999), 202pp. Office for Official Publications of the European Communities, Luxembourg. CEC (1985). Soil Map of the European Communities, 1:1,000,000. CEC Luxembourg, 124pp, 7 maps. Daroussin, J. and King, D. (1997). A pedotransfer rules database to interpret the Soil Geographical Database of Europe for environmental purposes. In: The use of pedotransfer in soil hydrology research in Europe. A. Bruand, O. Duval, H. Wosten, A. Lilly (eds). European Soil Bureau Research Report No.3. EUR EN, INRA, Orleans, France. EC (2002). Communication of 16 April 2002 from the Commission to the Council, the European Parliament, the Economic and Social Committee and the Committee of the Regions - Towards a Thematic Strategy for Soil Protection [COM (2002) 179 final 35pp. Heineke, H.J., Eckelmann, W., Thomasson, A.J., Jones, R.J.A., Montanarella, L. and Buckley, B. (eds). (1998). Land Information Systems: Developments for planning the sustainable use of land resources. European Soil Bureau Research Report No.4, EUR EN, 546pp. Office for Official Publications of the European Communities, Luxembourg. Hiederer, R. (2001) European Catchment Information System for Agri-Environmental Issues. Proceedings of EuroConference Link GEO and Water Research Genoa - Italy, 7-9 February Howard, P.J.A., Loveland, P.J., Bradley, R.I., Dry, F.T., Howard, D.M. and Howard, D.C. (1995). The carbon content of soil and its geographical distribution in Great Britain. Soil Use and Management 11, King, D., Daroussin, J. and Tavernier, R. (1994). Development of a soil geographical database from the soil map of the European Communities. Catena 21, King, D., Jones, R.J.A. and Thomasson, A.J. (1995). European Land Information Systems for Agro-environmental Monitoring. EUR EN, 285pp. Office for Official Publications of the European Communities, Luxembourg. Jones, R.J.A, R. Hiederer, E. Rusco, P.J. Loveland and L. Montanarella (2003). Topsoil Organic Carbon in Europe. Proceedings of the 4th European Congress on Regional Geoscientific Cartography and Information Systems, Bologna, , p Jones, R.J.A. and Hollis, J.M. (1996). Pedotransfer rules for environmental interpretations of the EU Soil Database. In: Soil databases to support sustainable development. C. Le Bas and M.,Jamagne (eds). EUR EN, Commission of the European Communities and INRA-Orleans. Le Bas, C. and Jamagne, M. (1996). Soil databases to support sustainable development. European Research Report No.2. EUR EN, 149pp. Office for Official Publications of the European Communities, Luxembourg. Madsen, H. Breuning and Jones, R.J.A. (1995). Soil Profile Analytical Database for the European Union. Danish Journal of Geography, 95, McGrath, S.P. and Loveland, P.J. (1992). The Soil Geochemical Atlas of England and Wales. Blackie Academic and Professional, London, 101pp. Rusco, E., Jones, R.J.A. and Bidoglio, G. (2001). Organic matter in the soils of Europe: Present status and future trends. EUR EN, 14pp. Office for Official Publications of the European Communities, Luxembourg. 4

5 Rusco, E. (In preparation). Carbon sequestration in Italy. Research Report, European Soil Bureau, European Commission Joint Research Centre, Ispra, 85pp. Smith, P. Powlson, D.S., Smith, J.U., Falloon, P., and Coleman, K. (2000a). Meeting the UK s climate change commitments: options for carbon mitigation on agricultural land. Soil Use and Management 16, Smith, P. Powlson, D.S., Smith, J.U., Falloon, P., and Coleman, K. (2000b). Revised estimates of the carbon mitigation potential of UK agricultural land. Soil Use and Management 16, Van Ranst, E., Thomasson, A.J., Daroussin, J., Hollis, J.M., Jones, R.J.A., Jamagne, M., King, D. and Vanmechelen, L. (1995). Elaboration of an extended knowledge database to interpret the 1:1,000,000 EU Soil Map for environmental purposes. In: European Land Information Systems for Agro-environmental Monitoring. D. King, R.J.A. Jones and A.J. Thomasson (eds.). EUR EN, Office for Official Publications of the European Communities, Luxembourg. Data sources Soil: European Soil Database (Fig. 29.3) The European Soil Database v.1.0 (King et al., 1995; Le Bas and Jamagne, 1996; Heineke et al., 1998) has been constructed from source material prepared and published at a scale of 1:1,000,000 (CEC, 1985). The resulting soil data are harmonised for the whole area covered according to a standard international classification (FAO-UNESCO, 1974; FAO-UNESCO- ISRIC, 1990), together with analytical data for standard profiles (Madsen and Jones, 1995). The spatial component of this database comprises polygons, which represent Soil Mapping Units (SMUs). These spatial elements are linked to one or more Soil Typological Units (STUs). Attribute data exist for STUs and so these data can be related to SMUs. Although each STU is unambiguously defined through a single set of parameters, SMUs may contain up to eight STUs. The spatial location of STUs within an SMU is not known, only the proportion of each STU in the SMU. In addition, many of the attributes are not stored as measured values but as codes relating to classes. While the structure of the European Soil Database allows for relatively efficient data storage, it is not particularly well suited for spatial analysis in raster format or combining external information, and for applying mathematical functions rather than rule-based systems. Therefore, a specific set of raster layers was produced based on an area-weighted distribution of attributes within SMUs. In the process, class values were transferred to continuous numeric values. Spatial representation of attributes was improved by performing a multi-criteria analysis within each SMU based on STU characteristics. The procedure provided a set of thematic layers, in which each soil parameter is stored in a single layer using continuous values for representation. Land Use/Cover: CORINE LC and USGS Eurasia (Fig. 29.4) The land cover data were taken from a data set covering Europe with information according to the CORINE Land Cover (LC) classification codes. Since CORINE LC covers only part of the area of interest, land cover was supplemented with specifically adapted Eurasia land cover data, derived from a US Geological Survey (USGS) database, in areas, where CORINE data are absent. To achieve comparable thematic coverage between the CORINE and USGS data, a series of cross-classifications was carried out, using various USGS data layers and reassigning or merging classes where appropriate. The final layer corresponds to CORINE level 3 classification codes. Climate: GHCN (Fig. 29.5) Variations in soil OC with varying climatic conditions were taken into account by using the average annual accumulated temperature (AAAT), expressed in day deg C. The data used were derived from station observations as available through the Global Historical Climatology Network GHCN (Easterling et al., 1996). From the point data, spatial layers were derived through a weighted distance interpolation. The influence of station altitude on temperature 5

6 observations was adjusted for by applying an adapted moist adiabatic lapse rate. The period used in the study covered the years The period was chosen, because it precedes the collection of ground data. However, moisture was not included separately as a parameter in the PTRs because the influence of this soil-forming factor is implicitly taken into account with the inclusion of the parameter "SOIL" in the PTR. Since the indicator aims to produce baseline data, i.e. a data layer of existing OC content, rather than modelling soil development and carbon stocks, using external datasets to model the influence of soil moisture could make the model unnecessarily complex. Figure 29.2 European Soil Database at scale 1:1,000,000 6

7 Figure 29.3 European Land Cover (R. Hiederer, 2002). 7

8 Figure 29.4 Average annual accumulated temperature (AAAT) computed from GHCN temperature data. AAAT day deg C

9 Methodological approach The procedure adopted was to first compute the rule-based set relating soil type, texture and land cover to OC and then to apply a function defining the influence of temperature. For the information on the soil parameters the European Soil Database (V. 1.0) was used. For texture, land cover and temperature, external detailed data sets substitute the sometimes incomplete data in this version of the soil database. Pedo-transfer Rules Pedo-transfer rules (PTRs) form the main component in the processing methodology developed for this indicator. The PTRs used, including one series for estimating topsoil OC (OC_TOP), were constructed for environmental interpretations of the European Soil Database (Van Ranst et al., 1995). Daroussin and King, (1997) and Jones and Hollis (1996) describe the general methodology for applying these PTRs. The OC_TOP rules use 6 input parameters: three for soil, one each for texture, land use, temperature. A total of 150 conditions or rules are defined in this series. The common form of using such rules is to apply them to the STUs of the European Soil Database and to generate a new attribute for each SMU. Information on ancillary parameters, such as land use, is used as found in the database. This indicator employs the PTRs in a different way than previously. Firstly, the rules are not applied to STUs in a table, but calculations are performed on spatial data layers directly. Secondly, external data are used for land use and temperature. The first step was to analyse the existing PTRs for OC_TOP (as defined by Van Ranst et al., 1995) to remove ambiguity and establish a logically consistent sequential order. The logical order was generally confirmed but some of the conditions were found to be ambiguous. More important to the study was the absence of any rules for soils with high organic carbon content (>12%). The next step was to assess duplicity of the rules when removing the temperature parameter and to evaluate, if additional modifications were required. Additional rules were then added to cover soils with high organic content. The result was a revised set of 141 rules with 24 modifications of existing conditions or additions. The revised PTRs, using 5 parameters of soil type, texture and land cover, were converted into script code of the Idrisi32 GIS and applied to the spatial data layers. The layers for soil and texture originate from a raster version of the European Soil Database (v.1.0), while the land use layer comprised re-classified extended CORINE data. The procedure resulted in a base-line map of OC_TOP for Europe, derived from PTRs applied to the European Soil Database, using spatially detailed and complete land cover but excluding the influence of temperature. Temperature Effect The influence of temperature on OC_TOP content was taken into account using a function rather than a rule-based approach. The main reason for this modification was the relatively crude classification of accumulated temperature (3 classes) used in the original PTRs. The function was developed in accordance with the established principle that, within belts of uniform moisture conditions and comparable vegetation, the average total organic matter and nitrogen in soils increase by two to three times for each 10 C fall in mean temperature (Buckman and Brady, 1960, p.152). This is only a very general relationship but it was thought to be suitable for a basic Pan-European indicator. Accordingly, the average annual accumulated temperature (AAAT) in Europe was computed for the reference period The relationship between AAAT (taaat) and temperature correction coefficient (TEMPcor) was defined by a sigmoidal function of the type: n ( t ) c TEMPcor = f * cos AAAT + The correction factor, (TEMPcor), was set to values between 0.5 and 1.8. At 4000 day C, TEMPcor = 1.0, i.e. the organic carbon values produced by the revised PTRs for soil and land use remain unchanged. Such AAAT values occur for example in southern England, northern 9

10 France and southern Germany. The TEMPcor reached 0.5 for areas in southern Europe with AAAT in the range 6500 to 7000 day C, which is in accordance with the observed increase in OC_TOP with an increase in average annual temperature of about 10 C. The maximum TEMPcor = 1.8, which corresponds to an AAAT of 1800 day C. Temperatures as low as this are experienced in northern Europe and in Alpine regions. The resulting function is depicted in Figure TEMPERATURE CORRECTION FOR OC Ceofficient for temperature variation (> 3 ground measurements) Correction Factor Temperature Range (AAAT deg. C) Semi-natural Cultivated Avg. Ground Data Model Figure 29.6 Correction Coefficient for Organic Carbon content by Temperature Variations The function used to calculate the coefficient for temperature correction TEMPcor was determined by the relationship found between temperature and soil OC and the parameters used in the calculation of organic matter. To determine the relationship OC-temperature an analysis of changes in OC in the ground data depending on AAAT was performed. Due to the distribution of the ground data the range of analysis was restricted to values of AAAT above 2000 C. According to the land use parameter in the PTR data points were determined separately for "semi-natural" and "cultivated" areas. Figure 29.6 depicts for the range of values found the data points, the weighted averages for ground measurements and the values used by the function. For the lower range of temperature values the development of the coefficient was limited to a factor of 1.8. The value was used, since the estimated organic matter (OM) content of a soil cannot exceed 100%, the climatic function was also constrained such that the maximum estimated OC_TOP content was approximately 60%, thus maintaining compatibility with the relatively stable OC:OM ratio of 1:1.72. The maximum correction value of 1.8 is supported be the averages provided from the analysis of the ground data. While in individual cases the coefficient exceeded a value of 2, the average at 2250 C was 1.7. The form of the correction coefficient above an AAAT of 1800 C was determined by the situation found for the ground data. The coefficient approaches a value of 0.5 for AAATs above 6500 C. The increase at temperatures above was attributed to the low number of observations (7). The best fit of the modelled data gave a coefficient of determination of

11 The relationship used in the model can only approximate the actual situation. For once, values for the coefficient exceeding a factor 1.8 were found in some cases. Yet, as a consequence of the values used in applying the PTR, the maximum could not be reasonably increased. Secondly, the behaviour of the relationship at AAAT below 2000 deg C could not be established from ground data. It was assumed that in this range a decrease in temperature would no longer increase OC. Quality information The results have been validated against data from National sources. Overall scoring (1=no major problems, 3=major reservations) Accuracy: 2 Comparability over time: as new Land Cover available (CLC2000) Comparability over space: 2 11