Agriculture has experienced a dramatic change during the past decades. The change has been structural and technological. Structural changes can be seen in the size of current farms; not long ago, agricultural production was organized around small farms, whereas nowadays the agricultural landscape is dominated by large farms. Large farms have better means of applying new technologies, and therefore technological advances have been a driving force in changing the farming structure. New technologies continue to emerge, and their mastery and use in requires that farmers gather more information and make more complex technological choices. In particular, the advent of the Internet has opened vast opportunities for communication and business opportunities within the agricultural com- nity. But at the same time, it has created another class of complex issues that need to be addressed sooner rather than later. Farmers and agricultural researchers are faced with an overwhelming amount of information they need to analyze and synthesize to successfully manage all the facets of agricultural production. This daunting challenge requires new and complex approaches to farm management. A new type of agricultural management system requires active cooperation among multidisciplinary and multi-institutional teams and ref- ing of existing and creation of new analytical theories with potential use in agriculture. Therefore, new management agricultural systems must combine the newest achievements in many scientific domains such as agronomy, economics, mathematics, and computer science, to name a few.

"This book presents high quality research on the design and implementation of information systems in the fields of agronomics, mathematics, economics, computer science, and the environment, offering holistic approaches to the design, development, and implementation of complex agricultural and environmental information systems"--Provided by publisher.

Software Engineering Techniques Applied to Agricultural Systems presents cutting-edge software engineering techniques for designing and implementing better agricultural software systems based on the object-oriented paradigm and the Unified Modeling Language (UML). The focus is on the presentation of rigorous step-by-step approaches for modeling flexible agricultural and environmental systems, starting with a conceptual diagram representing elements of the system and their relationships. Furthermore, diagrams such as sequential and collaboration diagrams are used to explain the dynamic and static aspects of the software system. This second edition includes: a new chapter on Object Constraint Language (OCL), a new section dedicated to the Model-VIEW-Controller (MVC) design pattern, new chapters presenting details of two MDA-based tools – the Virtual Enterprise and Olivia Nova and a new chapter with exercises on conceptual modeling. It may be highly useful to undergraduate and graduate students as the first edition has proven to be a useful supplementary textbook for courses in mathematical programming in agriculture, ecology, information technology, agricultural operations research methods, agronomy and soil science and applied mathematical modeling. The book has broad appeal for anyone involved in software development projects in agriculture and to researchers in general who are interested in modeling complex systems. From the reviews of the first edition: "The book will be useful for those interested in gaining a quick understanding of current software development techniques and how they are applied in practice... this is a good introductory text on the application of OOAD, UML and design patters to the creation of agricultural systems. It is technically sound and well written." —Computing Reviews, September 2006

For nearly a century, scientific advances have fueled progress in U.S. agriculture to enable American producers to deliver safe and abundant food domestically and provide a trade surplus in bulk and high-value agricultural commodities and foods. Today, the U.S. food and agricultural enterprise faces formidable challenges that will test its long-term sustainability, competitiveness, and resilience. On its current path, future productivity in the U.S. agricultural system is likely to come with trade-offs. The success of agriculture is tied to natural systems, and these systems are showing signs of stress, even more so with the change in climate. More than a third of the food produced is unconsumed, an unacceptable loss of food and nutrients at a time of heightened global food demand. Increased food animal production to meet greater demand will generate more greenhouse gas emissions and excess animal waste. The U.S. food supply is generally secure, but is not immune to the costly and deadly shocks of continuing outbreaks of food-borne illness or to the constant threat of pests and pathogens to crops, livestock, and poultry. U.S. farmers and producers are at the front lines and will need more tools to manage the pressures they face. Science Breakthroughs to Advance Food and Agricultural Research by 2030 identifies innovative, emerging scientific advances for making the U.S. food and agricultural system more efficient, resilient, and sustainable. This report explores the availability of relatively new scientific developments across all disciplines that could accelerate progress toward these goals. It identifies the most promising scientific breakthroughs that could have the greatest positive impact on food and agriculture, and that are possible to achieve in the next decade (by 2030).

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.

Completely devoted to applicati on of models to opti mize the use of limited water and nutrients in various climates, this collecti on will inspire confi dence in the capacity of modeling to tackle the biggest threats to secure agriculture. To obtain the most producti on from available water while maintaining natural resources, we need whole system–based quanti tati ve knowledge and tools to help select appropriate crops and manage water and associated inputs on a site-specifi c basis under changing climate. Site-specifi c experimental results are available for limited locati ons, limited periods of ti me, and limited management opti ons. Well-tested process models of cropping systems can extend fi eld research results to long-term weather conditi ons, as well as other climates and soils, allowing us to explore new management opti ons. The case studies in this volume are promising examples of these kinds of soluti ons.

Decision makers, such as government officials, need to better understand human activity in order to make informed decisions. With the ability to measure and explore geographic space through the use of geospatial intelligence data sources including imagery and mapping data, they are better able to measure factors affecting the human population. As a broad field of study, geospatial research has applications in a variety of fields including military science, environmental science, civil engineering, and space exploration. Geospatial Intelligence: Concepts, Methodologies, Tools, and Applications explores multidisciplinary applications of geographic information systems to describe, assess, and visually depict physical features and to gather data, information, and knowledge regarding human activity. Highlighting a range of topics such as geovisualization, spatial analysis, and landscape mapping, this multi-volume book is ideally designed for data scientists, engineers, government agencies, researchers, and graduate-level students in GIS programs.

Understanding the advancement of sustainable development is critical to managing human activities to avoid the overexploitation of resources and pollution of the environment beyond tolerable levels. Sustainable development involves not only preservation and care of the environment, but also recognition of the complex relations between economic, social and living systems. Environmental Modeling for Sustainable Regional Development: System Approaches and Advanced Methods presents processing methods and their applications, which are practical for decision making and task management at the regional level as well as for scientific studies in sustainable development assessment. This book serves as a reference guide for post-graduate students in the field of management as well as a critical guide for managers, government officials, and information professionals.

Risk, Reliability and Safety contains papers describing innovations in theory and practice contributed to the scientific programme of the European Safety and Reliability conference (ESREL 2016), held at the University of Strathclyde in Glasgow, Scotland (25—29 September 2016). Authors include scientists, academics, practitioners, regulators and other key individuals with expertise and experience relevant to specific areas. Papers include domain specific applications as well as general modelling methods. Papers cover evaluation of contemporary solutions, exploration of future challenges, and exposition of concepts, methods and processes. Topics include human factors, occupational health and safety, dynamic and systems reliability modelling, maintenance optimisation, uncertainty analysis, resilience assessment, risk and crisis management.

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.