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This second edition of Working with Dynamic Crop Models is meant for self-learning by researchers or for use in graduate level courses devoted to methods for working with dynamic models in crop, agricultural, and related sciences. Each chapter focuses on a particular topic and includes an introduction, a detailed explanation of the available methods, applications of the methods to one or two simple models that are followed throughout the book, real-life examples of the methods from literature, and finally a section detailing implementation of the methods using the R programming language. The consistent use of R makes this book immediately and directly applicable to scientists seeking to develop models quickly and effectively, and the selected examples ensure broad appeal to scientists in various disciplines. 50% new content – 100% reviewed and updated Clearly explains practical application of the methods presented, including R language examples Presents real-life examples of core crop modeling methods, and ones that are translatable to dynamic system models in other fields
Mathematical models are being used more and more widely to study complex dynamic systems (global weather, ecological systems, hydrological systems, nuclear reactors etc. including the specific subject of this book, crop-soil systems). The models are important aids in understanding, predicting and managing these systems. Such models are complex and imperfect. One fundamental research direction is to seek a better understanding of how these systems function, and to propose mathematical expressions embodying that understanding. However, this is not sufficient. It is also essential to have tools (often mathematical and statistical methods) to aid in developing, improving and using the models built from those equations. The book is specifically concerned with the application of methods to crop models, but much of the material is also applicable to dynamic system models in other fields. The goal of this book is to fill that gap. * State-of-the-art methods explained simply and illustrated specifically for crop models* Parameter estimation – applying statistical methods to the complex case of crop models, including Bayesian methods * Includes model evaluation, understanding and estimating prediction error* Offers a unique data assimilation by using the Kalman filter and beyond
Crop models and remote sensing techniques have been combined and applied in agriculture and crop estimation on local and regional scales, or worldwide, based on the simultaneous development of crop models and remote sensing. The literature shows that many new remote sensing sensors and valuable methods have been developed for the retrieval of canopy state variables and soil properties from remote sensing data for assimilating the retrieved variables into crop models. At the same time, remote sensing has been used in a staggering number of applications for agriculture. This book sets the context for remote sensing and modelling for agricultural systems as a mean to minimize the environmental impact, while increasing production and productivity. The eighteen papers published in this Special Issue, although not representative of all the work carried out in the field of Remote Sensing for agriculture and crop modeling, provide insight into the diversity and the complexity of developments of RS applications in agriculture. Five thematic focuses have emerged from the published papers: yield estimation, land cover mapping, soil nutrient balance, time-specific management zone delineation and the use of UAV as agricultural aerial sprayers. All contributions exploited the use of remote sensing data from different platforms (UAV, Sentinel, Landsat, QuickBird, CBERS, MODIS, WorldView), their assimilation into crop models (DSSAT, AQUACROP, EPIC, DELPHI) or on the synergy of Remote Sensing and modeling, applied to cardamom, wheat, tomato, sorghum, rice, sugarcane and olive. The intended audience is researchers and postgraduate students, as well as those outside academia in policy and practice.
Can we unlock resilience to climate stress by better understanding linkages between the environment and biological systems? Agroclimatology allows us to explore how different processes determine plant response to climate and how climate drives the distribution of crops and their productivity. Editors Jerry L. Hatfield, Mannava V.K. Sivakumar, and John H. Prueger have taken a comprehensive view of agroclimatology to assist and challenge researchers in this important area of study. Major themes include: principles of energy exchange and climatology, understanding climate change and agriculture, linkages of specific biological systems to climatology, the context of pests and diseases, methods of agroclimatology, and the application of agroclimatic principles to problem-solving in agriculture.
Crop modelling has huge potential to improve decision making in farming. This collection reviews advances in next-generation models focused on user needs at the whole farm system and landscape scale.
This easy-to-follow book presents the fundamentals of how to measure vision and visual function. It offers a solid understanding of the neural basis of vision where it is known or can be deduced. Each chapter features an overview summarizing the chapter, declarative headings throughout for instant review, study guide questions, and a glossary. Explanations of the neural mechanisms underlying visual function help readers understand how vision is measured. It details the scientific basis of how visual function is measured and applies laboratory measurement to clinical procedures.
This book is a textbook (it includes, for example, exercises and outline solutions). The plant scientist is shown how to express physiological ideas mathematically and how to deduce quantitative conclusions, which can then be compared with experiment. There is little new biology in the book, but it is presented in a way that will be new to many biologists. The matching of models to experiments means using mathematics for formulating biological concepts and second, using algebra, calculus, or, now more frequently, computers to solve or simulate the resulting model; and finally, comparing, qualitatively or quantitatively, prediction to measurement. Computers are the important enabling technology that makes it all possible: solving equations, assembling models of increasing sophistication and complexity, and comparing theory with experiment. The book is divided into three parts. Part I. Covers subjects of wide relevance to modelling and plant biology. Part II. The reader may choose to select topics of particular interest from part II. However, the whole-plant modeller will need to study all chapters, and the plant ecosystem modeller may need to add other material also. Part III. Plant morphology is at an introductory level. It is included because morphological characters may prove to be of equal importance to some physiological traits in determining plant function and performance. "This textbook presents, in an interesting and clearly written fashion, a mathematical approach to a wide range of topics in plant and crop physiology, including light interception, leaf and canopy photosynthesis, respiration, partitioning, transpiration and water relations, branching and phyllotaxis. The biochemistry of plant growth and maintenanace is also presented in some detail. I was very pleased with the text, especially with the philosophy presented by the authors that biological models are necessarily simplifications of complex detail. I would strongly recommend it for reading and consultation by graduates and research workers." J. Exp. Botany "The authors' approach succeeds admirably, giving a thorough account of the mathematical toolbox available to researchers and the areas in which those tools have been used." Plant, Cell and Environment "Combining considerable technical cleverness with creativity and the refreshing notion that science is a "common-sense, unpredictable, fascinating and thoroughly human activity." Times Higher Educational Supplement "Exceptionally scholarly volume. Logical and systematic. Authors have assembled a mass of mathematical material in an elegant layout." Agricultural Systems
From controlling disease outbreaks to predicting heart attacks, dynamic models are increasingly crucial for understanding biological processes. Many universities are starting undergraduate programs in computational biology to introduce students to this rapidly growing field. In Dynamic Models in Biology, the first text on dynamic models specifically written for undergraduate students in the biological sciences, ecologist Stephen Ellner and mathematician John Guckenheimer teach students how to understand, build, and use dynamic models in biology. Developed from a course taught by Ellner and Guckenheimer at Cornell University, the book is organized around biological applications, with mathematics and computing developed through case studies at the molecular, cellular, and population levels. The authors cover both simple analytic models--the sort usually found in mathematical biology texts--and the complex computational models now used by both biologists and mathematicians. Linked to a Web site with computer-lab materials and exercises, Dynamic Models in Biology is a major new introduction to dynamic models for students in the biological sciences, mathematics, and engineering.
Agricultural yields have increased steadily in the last half century, particularly since the Green Revolution. At the same time, inflation-adjusted agricultural commodity prices have been trending downward as increases in supply outpace the growth of demand. Recent severe weather events, biofuel mandates, and a switch toward a more meat-heavy diet in emerging economies have nevertheless boosted commodity prices. Whether this is a temporary jump or the beginning of a longer-term trend is an open question. Agricultural Productivity and Producer Behavior examines the factors contributing to the remarkably steady increase in global yields and assesses whether yield growth can continue. This research also considers whether agricultural productivity growth has been, and will be, associated with significant environmental externalities. Among the topics studied are genetically modified crops; changing climatic factors; farm production responses to government regulations including crop insurance, transport subsidies, and electricity subsidies for groundwater extraction; and the role of specific farm practices such as crop diversification, disease management, and water-saving methods. This research provides new evidence that technological as well as policy choices influence agricultural productivity.