Title: Using soil fertility index to evaluate two different sampling schemes in soil fertility mapping: A case study of Hvanneyri, Iceland

Soil test-based fertility management has been one of the effective tools for increasing productivity of agricultural soils that have a high degree of spatial variability. Changes in land use and land cover are important to the study of global environmental change issues. Among these issues are soil fertility depletion and management. Many times, stakeholders and policy makers overlook this issue when designing and implementing policies for land restoration and sustainable management. Nutrient pattern availability and distribution need to be known so as to determine factors that contribute to their depletion. An alternative and promising approach to our traditional analytical method which has become a vital tool in most decision making processes is the use of Geographic Information System (GIS) analytical tools. GIS based soil fertility maps outline a cost effective option for implementing improved nutrient management in large tracts. With the incorporation of this method, agricultural areas with very high or low nutrient loadings can easily be determined to enable the development of appropriate and economically sound management recommendations. The main goal of this study was to develop georeferenced soil fertility maps showing distributions of soil nutrients and their spatial variability. The spatial variability was assessed using soil fertility index (SFI). Assessment of nutrient distribution and trend patterns were estimated before the development of nutrient distribution surface maps. Also, minimum soil fertility indicators (MSFI) were integrated into SFI and then used in the development of probability threshold maps. Laboratory analyses of soil samples were used to estimate the composition of soil fertility indicators. The following MSFI were determined: total soil Carbon (Ctot) and Nitrogen (Ntot), soil KCl extractable N ions (NH4 + -N and NO3- -N), soil pH, biomass C (MICc), temperature and rainfall, metabolic quotient (qCO2) and soil moisture content. Models for soil fertility distribution were then applied to the data to derive fertility indicators for mapping. In addition, geostatistical analysis was applied to all MSFI with land use and land cover (LULC) data in mind. By computing SFI from sampled sites, SFI revealed the pattern of nutrient distribution in each measured unit. SFI values were then used to develop the choropleth maps and threshold probability maps in making recommendations on soil spatial variability in fertility management.