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Provides an essential introduction to modeling terrestrial ecosystems in Earth system models for graduate students and researchers.
The first premise of this book is that farmers need access to options for improving their situation. In agricultural terms, these options might be manage ment alternatives or different crops to grow, that can stabilize or increase household income, that reduce soil degradation and dependence on off-farm inputs, or that exploit local market opportunities. Farmers need a facilitating environment, in which affordable credit is available if needed, in which policies are conducive to judicious management of natural resources, and in which costs and prices of production are stable. Another key ingredient of this facilitating environment is information: an understanding of which options are viable, how these operate at the farm level, and what their impact may be on the things that farmers perceive as being important. The second premise is that systems analysis and simulation have an impor tant role to play in fostering this understanding of options, traditional field experimentation being time-consuming and costly. This book summarizes the activities of the International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) project, an international initiative funded by the United States Agency for International Development (USAID). IBSNAT was an attempt to demonstrate the effectiveness of understanding options through systems analysis and simulation for the ultimate benefit of farm households in the tropics and subtropics. The idea for the book was first suggested at one of the last IBSNAT group meetings held at the University of Hawaii in 1993.
This book reports on developments in Proximal Soil Sensing (PSS) and high resolution digital soil mapping. PSS has become a multidisciplinary area of study that aims to develop field-based techniques for collecting information on the soil from close by, or within, the soil. Amongst others, PSS involves the use of optical, geophysical, electrochemical, mathematical and statistical methods. This volume, suitable for undergraduate course material and postgraduate research, brings together ideas and examples from those developing and using proximal sensors and high resolution digital soil maps for applications such as precision agriculture, soil contamination, archaeology, peri-urban design and high land-value applications, where there is a particular need for high spatial resolution information. The book in particular covers soil sensor sampling, proximal soil sensor development and use, sensor calibrations, prediction methods for large data sets, applications of proximal soil sensing, and high-resolution digital soil mapping. Key themes: soil sensor sampling – soil sensor calibrations – spatial prediction methods – reflectance spectroscopy – electromagnetic induction and electrical resistivity – radar and gamma radiometrics – multi-sensor platforms – high resolution digital soil mapping - applications Raphael A. Viscarra Rossel is a scientist at the Commonwealth Scientific and Industrial Research Organisation (CSIRO) of Australia. Alex McBratney is Pro-Dean and Professor of Soil Science in the Faculty of Agriculture Food & Natural Resources at the University of Sydney in Australia. Budiman Minasny is a Senior Research Fellow in the Faculty of Agriculture Food & Natural Resources at the University of Sydney in Australia.
Soil organic matter (SOM) represents a major pool of carbon within the biosphere, roughly twice than in atmospheric CO2. SOM models embody our best understanding of soil carbon dynamics and are needed to predict how global environmental change will influence soil carbon stocks. These models are also required for evaluating the likely effectiveness of different mitigation options. The first important step towards systematically evaluating the suitability of SOM models for these purposes is to test their simulations against real data. Since changes in SOM occur slowly, long-term datasets are required. This volume brings together leading SOM model developers and experimentalists to test SOM models using long-term datasets from diverse ecosystems, land uses and climatic zones within the temperate region.
The concept of using mathematical models to investigate crop growth and productivity has attracted much attention in recent years. A major reason is that modelling can allow an expert in one area to assess the impact of his ideas in the light of other advances in our understanding of crop performance. Whether or not many of the claims made for or the demands made of models can ever be satisfied, this role as a focus for quantitative definition of crop growth is an important one. One consequence is that the development and appraisal of such models requires the efforts of scientists from a wide range of disciplines. This NATO Advanced Research workshop was designed to bring together such a range of scientists to consider the wheat crop, and assess our understanding of the crop and our ability to model its growth and yield. The ideas and organization behind the workshop involved many people. The U. K. interest in a computer model of wheat growth was instigated by Dr. Joe Landsberg (then of Long Ashton Research Station, but now Director of CSIRO Division of Forest Research), who in 1979 started a modelling exercise as part of a collaborative study of the causes of yield variation in winter wheat, involving four research institutes supported by the Agricultural and Food Research Council. Dr.
The two volumes IFIP AICT 545 and 546 constitute the refereed post-conference proceedings of the 11th IFIP WG 5.14 International Conference on Computer and Computing Technologies in Agriculture, CCTA 2017, held in Jilin, China, in August 2017. The 100 revised papers included in the two volumes were carefully reviewed and selected from 282 submissions. They cover a wide range of interesting theories and applications of information technology in agriculture. The papers focus on four topics: Internet of Things and big data in agriculture, precision agriculture and agricultural robots, agricultural information services, and animal and plant phenotyping for agriculture.
The practitioner or researcher often faces complex alternatives when selecting a method to characterize properties governing a soil process. After years of research and development, environmental and agricultural professionals now have an array of methods for characterizing soil processes. Well-established methods, however, may not be suitable for