Wheat, which is the second most important cereal crop in the world, is being grown in a wide range of climates over an area of about 228 945 thou­ sand ha with a production of about 535 842 MT in the world. Bread wheat (Triticum aestivum L. ) accounts for 80% of the wheat consumption, howe­ ver, it is attacked by a large number of pests and pathogens; rusts and smuts cause enormous damage to the crop and reduce the yield drastically in some areas. The major breeding objectives for wheat include grain yield, earliness, resistance to lodging and diseases, spikelet fertility, cold tolerance, leaf duration and net assimilation rate, fertilizer utilization, coleoptile length, nutritional value, organoleptic qualities, and the improvement of charac­ ters such as color and milling yield. The breeding of wheat by traditional methods has been practiced for centuries, however, it has only now come to a stage where these methods are insufficient to make any further breakthrough or to cope with the world's demand. Although numerous varieties are released every year around the world, they do not last long, and long-term objectives cannot be realized unless more genetic variability is generated. Moreover, the intro­ duction of exotic genetic stocks and their cultivation over large areas results in the depletion and loss of the native germplasm pool.

Multiscale Modeling (Biotechnology in Agriculture & Forestry)

Science & Math

Ferreira, Marco A.R.

Lee, Herbert K.H.

Biological Sciences

Springer

<p>This highly useful book contains methodology for the analysis of data that arise from multiscale processes. It brings together a number of recent developments and makes them accessible to a wider audience. Taking a Bayesian approach allows for full accounting of uncertainty, and also addresses the delicate issue of uncertainty at multiple scales. These methods can handle different amounts of prior knowledge at different scales, as often occurs in practice.</p>

Multiscale Modeling: A Bayesian Perspective (Springer Series in Statistics)

Business & Money

Economics

Time Series (Chapman & Hall/CRC Texts in Statistical Science)

West, Mike

Prado, Raquel

Mathematics

Chapman and Hall/CRC

<p>This is the second edition of a popular graduate level textbook on time series modeling, computation and inference. The book is essentially unique in its approach, with a focus on Bayesian methods, although classical methods are also covered. The second edition has been updated with an additional author, new references throughout, and new material on multivariate time series. There are also lots of new examples and exercises, and the computing has been greatly enhanced. Code, data, and other material are now made available on a supplementary website.</p>

Time Series: Modeling, Computation, and Inference, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)

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