Improving Modeling Tools To Assess Climate Change Effects On Crop Response
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Improving Modeling Tools to Assess Climate Change Effects on Crop Response
Author: Jerry L. Hatfield
language: en
Publisher: John Wiley & Sons
Release Date: 2020-01-22
Crop model intercomparison and improvement are required to advance understanding of the impact of future climate change on crop growth and yield. The initial efforts undertaken in the Agriculture Model Intercomparison and Improvement Project (AgMIP) led to several observations where crop models were not adequately simulating growth and development. These studies revealed where enhanced efforts should be undertaken in experimental data to quantify the carbon dioxide × temperature × water interactions in plant growth and yield. International leaders in this area held a symposium at the 2013 ASA, CSSA, and SSSA Annual Meeting to discuss this topic. This volume in the Advances in Agricultural Systems Modeling series presents experimental observations across crops and simulation modeling outcomes and addresses future challenges in improving crop simulation models. IN PRESS! This book is being published according to the “Just Published” model, with more chapters to be published online as they are completed.
Computational Modeling Applications for Climate Crisis
Computational Modeling Applications for Climate Crisis provides readers with innovative research on the applications of computational modeling to moderate climate change. The book begins with an overview and history of climate change, followed by several chapters covering the concepts of computational modeling and simulation, including parameters of climate change, modeling the effects of human activities, visualization tools, and data fusion for advanced modeling applications. It then proceeds to cover decision support systems, modeling of technological solutions for climate change, modeling of greenhouse gas emissions, tracking of climate factors, and modeling of earth resources. In the final chapters of the book, the authors cover nation-based outcomes, big data, and optimization solutions with real-world data and case studies. Climate change is one of the most pressing existential issues for humans and the planet, and this book covers leading-edge applications of computational modeling to the vast array of interdisciplinary factors and challenges posed by climate change. As life itself is a mixture of occurrences that can be mathematically modelled, it is important to work with specific parameters, which are critical for monitoring and controlling the dynamics of the earth, natural resources, technological factors, and human activities. - Illustrates how computational modeling techniques can be used for dealing with the climate crisis, including simulations, multi-mode-data, usage, and visualization-based research - Provides case studies demonstrating innovative solutions to moderate climate change, including mathematical, visual, and multi-data-based findings of applied research - Authored by leading researchers in computational modeling
Working with Dynamic Crop Models
Working with Dynamic Crop Models: Methods, Tools and Examples for Agriculture and Environment, 3e, is a complete guide to working with dynamic system models, with emphasis on models in agronomy and environmental science. The introductory section presents the foundational information for the book including the basics of system models, simulation, the R programming language, and the statistical notions necessary for working with system models. The most important methods of working with dynamic system models, namely uncertainty and sensitivity analysis, model calibration (frequentist and Bayesian), model evaluation, and data assimilation are all treated in detail, in individual chapters. New chapters cover the use of multi-model ensembles, the creation of metamodels that emulate the more complex dynamic system models, the combination of genetic and environmental information in gene-based crop models, and the use of dynamic system models to aid in sampling. The book emphasizes both understanding and practical implementation of the methods that are covered. Each chapter simply and clearly explains the underlying principles and assumptions of each method that is presented, with numerous examples and illustrations. R code for applying the methods is given throughout. This code is designed so that it can be adapted relatively easily to new problems. - An expanded introductory section presents the basics of dynamic system modeling, with numerous examples from multiple fields, plus chapters on numerical simulation, statistics for modelers, and the R language - Covers in detail the basic methods: uncertainty and sensitivity analysis, model calibration (both frequentist and Bayesian), model evaluation, and data assimilation - Every method chapter has numerous examples of applications based on real problems, as well as detailed instructions for applying the methods to new problems using R - Each chapter has multiple exercises for self-testing or for classroom use - An R package with much of the code from the book can be freely downloaded from the CRAN package repository