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The study was on the performance of the decision support system for agrotechnology transfer (DSSAT) and the soil water atmosphere plant (SWAP) under an acid sulphate soil. The comparison of these models was done as a prerequisite to the selection of an appropriate model, which is capable of simulating water management scenarios, water balance and crop growth, to be coupled with an adaptive optimization algorithm that can be used to explore water management options.
This book discusses the development of useful models and their applications in soil and water engineering. It covers various modeling methods, including groundwater recharge estimation, rainfall-runoff modeling using artificial neural networks, development and application of a water balance model and a HYDRUS-2D model for cropped fields, a multi-model approach for stream flow simulation, multi-criteria analysis for construction of groundwater structures in hard rock terrains, hydrologic modeling of watersheds using remote sensing, and GIS and AGNPS.
This publication comes with computer software and presents a comprehensive simulation model designed to predict the hydrologic response, including potential for surface and groundwater contamination, of alternative crop-management systems. It simulates crop development and the movement of water, nutrients and pesticides over and through the root zone for a representative unit area of an agricultural field over multiple years. The model allows simulation of a wide spectrum of management practices and scenarios with special features such as the rapid transport of surface-applied chemicals through macropores to deeper depths and the preferential transport of chemicals within the soil matrix via mobile-immobile zones. The transfer of surface-applied chemicals (pesticides in particular) to runoff water is also an important component.
Highlighting effective, analytical functions that have been found useful for the comparison of alternative management techniques to maximize water and nutrient resources, this reference describes the application of viable mathematical models in data analysis to increase crop growth and yields. Featuring solutions to various differential equations,