Working with Dynamic Crop Models

Mathematical models are being used increasingly to study complex dynamic systems. Through the use of these mathematical equations, researchers are better able to understand of the development and improvement of these dynamic systems. Working with Dynamic Crop Models proposes such mathematical equations and offers the latest research and tools for building, analyzing, evaluating and using dynamic crop models. This book provides detailed, complex methods, starting from basic principles and simple applications, to a gradual explanation of state-of-the-art methods and their use. The editors provide vital ideas on improving model predictions, optimizing management decisions, and genetically enhancing crops. It is valuable for researchers, graduates studying crop modeling, biologists, agronomists, engineers, and plant physiologists who require and understanding for soil-plant-atmosphere system. Many different mathematical and statistical methods are essential in crop modeling. They are necessary in the development, analysis and application of crop models. Up to now, however, there has been no single source where crop modelers could learn about these methods. Furthermore, these methods are often described in other contexts and their application to crop modeling is not always straightforward. This book aims at making a large range of relevant mathematical and statistical methods accessible to crop modelers. Each methodology chapter starts from basic principles and simple applications and builds gradually to state-of-the-art methods. Crop models are used as examples, and practical advice on applying the methods to crop models is given. "Working with Dynamic Crop Models" is an essential learning and reference resource for students and researchers who want to understand and apply rigorous methods to crop models. This book will also be of value for other fields which use dynamic models of complex systems. Topics covered include: Parameter estimation - including Bayesian methods; Model evaluation - including prediction quality and decision quality; Sensitivity analysis - including global analysis and interactions; Data assimilation - the Kalman filter and extensions; Management optimization - including stochastic optimization; Models for multiple fields - emphasizing how to obtain input values; and Crop models and crop breeding - recent advances in using crop models.