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This open-access textbook provides a comprehensive, up-to-date guide for students and practitioners wishing to access in a single volume the key disciplines and principles of wheat breeding. Wheat is a cornerstone of food security: it is the most widely grown of any crop and provides 20% of all human calories and protein. The authorship of this book includes world class researchers and breeders whose expertise spans cutting-edge academic science all the way to impacts in farmers’ fields. The book’s themes and authors were selected to provide a didactic work that considers the background to wheat improvement, current mainstream breeding approaches, and translational research and avant garde technologies that enable new breakthroughs in science to impact productivity. While the volume provides an overview for professionals interested in wheat, many of the ideas and methods presented are equally relevant to small grain cereals and crop improvement in general. The book is affordable, and because it is open access, can be readily shared and translated -- in whole or in part -- to university classes, members of breeding teams (from directors to technicians), conference participants, extension agents and farmers. Given the challenges currently faced by academia, industry and national wheat programs to produce higher crop yields --- often with less inputs and under increasingly harsher climates -- this volume is a timely addition to their toolkit.

Agricultural genome mapping. --- Agriculture. --- Food science. --- Medicine—Research. --- Biology—Research. --- Agricultural Genetics. --- Food Science. --- Translational Research. --- Food technology --- Chemical engineering --- Farming --- Husbandry --- Industrial arts --- Life sciences --- Food supply --- Land use, Rural --- Gene mapping --- crop breeding --- breeding technologies --- genetic gains --- crop yield potential --- wheat pathology --- global economics of wheat --- agriculture public-private parternship --- Blat --- Genètica vegetal --- Millorament selectiu de plantes

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This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment.

Technology: general issues --- History of engineering & technology --- Environmental science, engineering & technology --- UAS --- multiple sensors --- vegetation index --- leaf nitrogen accumulation --- plant nitrogen accumulation --- pasture quality --- airborne hyperspectral imaging --- random forest regression --- sun-induced chlorophyll fluorescence (SIF) --- SIF yield indices --- upward --- downward --- leaf nitrogen concentration (LNC) --- wheat (Triticum aestivum L.) --- laser-induced fluorescence --- leaf nitrogen concentration --- back-propagation neural network --- principal component analysis --- fluorescence characteristics --- canopy nitrogen density --- radiative transfer model --- hyperspectral --- winter wheat --- flooded rice --- pig slurry --- aerial remote sensing --- vegetation indices --- N recommendation approach --- Mediterranean conditions --- nitrogen --- vertical distribution --- plant geometry --- remote sensing --- maize --- UAV --- multispectral imagery --- LNC --- non-parametric regression --- red-edge --- NDRE --- dynamic change model --- sigmoid curve --- grain yield prediction --- leaf chlorophyll content --- red-edge reflectance --- spectral index --- precision N fertilization --- chlorophyll meter --- NDVI --- NNI --- canopy reflectance sensing --- N mineralization --- farmyard manures --- Triticum aestivum --- discrete wavelet transform --- partial least squares --- hyper-spectra --- rice --- nitrogen management --- reflectance index --- multiple variable linear regression --- Lasso model --- Multiplex®3 sensor --- nitrogen balance index --- nitrogen nutrition index --- nitrogen status diagnosis --- precision nitrogen management --- terrestrial laser scanning --- spectrometer --- plant height --- biomass --- nitrogen concentration --- precision agriculture --- unmanned aerial vehicle (UAV) --- digital camera --- leaf chlorophyll concentration --- portable chlorophyll meter --- crop --- PROSPECT-D --- sensitivity analysis --- UAV multispectral imagery --- spectral vegetation indices --- machine learning --- plant nutrition --- canopy spectrum --- non-destructive nitrogen status diagnosis --- drone --- multispectral camera --- SPAD --- smartphone photography --- fixed-wing UAV remote sensing --- random forest --- canopy reflectance --- crop N status --- Capsicum annuum --- proximal optical sensors --- Dualex sensor --- leaf position --- proximal sensing --- cross-validation --- feature selection --- hyperparameter tuning --- image processing --- image segmentation --- nitrogen fertilizer recommendation --- supervised regression --- RapidSCAN sensor --- nitrogen recommendation algorithm --- in-season nitrogen management --- nitrogen use efficiency --- yield potential --- yield responsiveness --- standard normal variate (SNV) --- continuous wavelet transform (CWT) --- wavelet features optimization --- competitive adaptive reweighted sampling (CARS) --- partial least square (PLS) --- grapevine --- hyperparameter optimization --- multispectral imaging --- precision viticulture --- RGB --- multispectral --- coverage adjusted spectral index --- vegetation coverage --- random frog algorithm --- active canopy sensing --- integrated sensing system --- discrete NIR spectral band data --- soil total nitrogen concentration --- moisture absorption correction index --- particle size correction index --- coupled elimination