Saturday, 15 July 2006

The Use of Soil Mapping with the Aim of Improving Farming Practices in a Region of Crete, Greece.

Evangelia Vavoulidou1, Elisabethe Avramidis1, Periclis Papadopoulos1, Athanasios Charoulis2, Theodoros Karyotis2, and Kostaninos Soulis3. (1) Soil Science Institute of Athens,NAGREF, 1 S.Venizelou str, ATHENS, 14125, Greece, (2) Inst.of Soil Mapping and Classification, Theofrastou str.:, Larissa, 41 1335, Greece, (3) Agricultural Univerisity of Athens,Dep.of Nat. Resources Devel. and Agr. Engineering, Iera Odos 75, Botanikos, ATHENS, Greece

In the period 2000 2003, a soil survey and mapping program, financed by the municipality of Malia in Crete, was carried out over a 5000 ha agricultural area. Sampling points were determined on the basis of land ownership, with representative producer's plots being sampled in order to cover the requirements of the study. The land area is used for high-income agriculture and is cultivated mainly with olives and potatoes and its climate type is moist meso-Mediterranean. According to USDA classification, the soils at higher altitudes were classified as Entisols xerorthents or xerofluvents and those at lower altitudes as Alfisols xeralfs. Most soils have a heavy texture (SCL), but the surface horizon of the Alfisols have a sandy loam texture. The Alfisols are calcareous free soils with an acid reaction between 3.8 and 6.5. In contrast, the Entisols have a higher pH (7.5) because of CaCO3 content, which has a mean value 4 cmol /kg The total amount of organic matter is very low, ranging between 0 and 50 g/kg. The spatial data from the soil survey were organized in several data layers with similar information and were processed using a geographical information system (GIS) in order to facilitate its storage, management and analysis. These layers had various formats and topologies depending on the type of information they contained, e.g. the slope data layer, the digital elevation model and the satellite imagery had raster format while the boundaries of the soil units, the roads and the settlements of the sampling Points had vector format. The first step was the collection, digitization and processing of all the available data for the study area. These were topography maps, geological maps, satellite imagery and aerial photographs. Based on this information, many other derivative data layers such as slope and aspect were calculated. Following this, sampling took place and the exact position of each sampling and observation point was recorded with the help of GPS devices. This information, together with all results pertaining to the sample, was entered into the graphical database, which was specially designed to store all the relevant data for each point. Maps showing the spatial distribution of key parameters such as soil texture, pH, CaCO3 and nutrient status were produced on the basis of data from the sampling points and interpolation using geostatistical methods that took into account parameters such as slope and relief. Through the use of GIS, it was possible to greatly facilitate soil classification and the definition of the soil unit boundaries. The visualization of all the data collected in the form of various thematic maps along with the evaluation of the maps of key soil parameters in the studied area demonstrate the ability of GIS not only to act as an important administrative tool but also to provide valuable support for farmers in improving farming practice.

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