Why model? Agricultural system models enhance and extend field research...to synthesize and examine experiment data and advance our knowledge faster, to extend current research in time to predict best management systems, and to prepare for climate-change effects on agriculture. The relevance of such models depends on their implementation. Methods of Introducing System Models into Agricultural Research is the ultimate handbook for field scientists and other model users in the proper methods of model use. Readers will learn parameter estimation, calibration, validation, and extension of experimental results to other weather conditions, soils, and climates. The proper methods are the key to realizing the great potential benefits of modeling an agricultural system. Experts cover the major models, with the synthesis of knowledge that is the hallmark of the Advances in Agricultural Systems Modeling series.

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

The two volumes IFIP AICT 478 and 479 constitute the refereed post-conference proceedings of the 9th IFIP WG 5.14 International Conference on Computer and Computing Technologies in Agriculture, CCTA 2015, held in Beijing, China, in September 2015. The 122 revised papers included in this volume were carefully selected from 237 submissions. They cover a wide range of interesting theories and applications of information technology in agriculture, including intelligent sensing, monitoring and automatic control technology; key technology and models of the Internet of things; intelligent technology for agricultural equipment; computer vision; computer graphics and virtual reality; computer simulation, optimization and modeling; cloud computing and agricultural applications; agricultural big data; decision support systems and expert systems; 3s technology and precision agriculture; quality and safety of agricultural products; detection and tracing technology; and agricultural electronic commerce technology.

Crop, livestock, and forestry productions systems are important sources and sinks of greenhouses gases, but estimates of the magnitude of gas fluxes are more uncertain than those for other economic sectors such as transportation and electricity generation. Recent improvements in process-level un-derstanding, modeling software, and observational data used for model testing have increased the accuracy of model predictions, but substantial uncertainty remains, particularly regarding the potential for different management practices to mitigate emissions. The chapters in this volume demonstrate that both simple methods and complex models have strengths and limitations depending on stake-holder interest, scale of application, and other factors. Future improvement can be facilitated by or-ganizing model input and testing data into web-accessible databases and by making model algorithms more available and transparent.

Achieving food security and economic developmental objectives in the face of climate change and rapid population growth requires systems modelling approaches, for example in the design of sustainable agriculture farming systems. Such approaches increase our understanding of system responses to different soil and climatic conditions, and provide insights into the effects of various variable climate change scenarios, providing valuable information for decision-makers. Further, in the agricultural sector, systems modelling can help optimise crop management and adaptation measures to boost productivity under variable climatic conditions. Presenting key outcomes from crop models used in agricultural systems this book is a valuable resource for professionals interested in using modelling approaches to manage the growth and improve the quality of various crops.

Sustainable Agriculture and the Environment describes the relationship of agriculture, society, nature and the environment, sustainable agriculture and sustainable development goals, management of biophysical resources for sustainable food and environment, traditional knowledge and innovative options, and social and policy aspects of sustainable agriculture. The book presents both environmental and economic principles, helping readers in the development and application of robust policy and good institutional systems that execute on sustainable agriculture practices for a healthy environment and to combat climate resilience. - Includes case studies that provide real-world insights - Relates traditional knowledge and innovation, maximizing the potential from both - Reinforces our understanding of the role of sustainable agriculture in developing environmentally sustainable and profitable food systems

This book covers all aspects related to climate change and agriculture. The book discusses Global Climate Models (GCMs), Coupled Model Intercomparison Project (CMIP) and application of strategic management tool that includes RCP (Representative concentration Pathway), SSP (Shared Socio-economic Pathways) and SPA (Shared climate Policy Assumptions). The book provides information on how climate change, agricultural productivity and food security are interlinked. The impacts of climate change on food security are studied through different climatic drivers e.g., ENSO (El Niño–Southern Oscillation) and SOI (Southern Oscillation Index). These drivers are responsible for the climatic extreme events hence early prediction of these drivers could help to design appropriate adaptive measures for the agriculture sector and could be considered as early warning tools for risk management. Similarly, climate change and process-based soil modeling as well as the role of soil microbes and climate smart agriculture are discussed in this book. Climate change impacts on legume crop production and adaptation strategies are presented, with details about cereal crop modeling, perspectives of Camelina sativa as well as low input biofuel and oilseed crop, greenhouse gases (GHGs) emissions and mitigation strategies.

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.

Conservation planning involves targeted management practices and land use decision-making based on careful analysis of landscape limitations in order to protect soil and water resources. Developing solutions to conservation planning is of worldwide interest due to anticipated population growth, growing demand of feedstocks for biofuels, decreasing