الفهرس 
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Abstract
This study aimed at building a soil testing information system to achieve three objectives:
(a)the first objective was to compute the nutrient availability index and soil characteristics from the chemical and physical analytical data;
(b)the second objective was to compute the nutrients critical levels using the soil test results, soil characteristics, and field information to provide a fertilizer recommendations using artificial neural network models; and
(c)the third objective was to manipulate the large volume of stored soil tests data and fertilizer recommendations that helps evaluating the long term changes in soil tests levels in different regions, and formulating fertilizer policies.
To achieve the above objectives a data processing system (DPS), a decision support system (DSS), a reports generator (RG), and a database have been built to form a soil testing information system. Data from the field information, and chemical-physical analytical processes are input through the DPS before being stored in the database. The DPS convert the raw data from the analytical results into a nutrient availability index and soil characteristics information that useful to decision makers. Next, the relational database system is used to manage, and store the large amounts of data. The DSS is used to compute the nutrient critical levels using a neural network model to provide the fertilizer recommendations based on the soil test results, soil characteristics, and field information which are stored in the database by the DPS.