Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: progress and challenges

Quantitative traits—be they morphological or physiological characters, aspects of behavior, or genome-level features such as the amount of RNA or protein expression for a specific gene—usually show considerable variation within and among populations. Quantitative genetics, also referred to as the genetics of complex traits, is the study of such characters and is based on mathematical models of evolution in which many genes influence the trait and in which non-genetic factors may also be important. Evolution and Selection of Quantitative Traits presents a holistic treatment of the subject, showing the interplay between theory and data with extensive discussions on statistical issues relating to the estimation of the biologically relevant parameters for these models. Quantitative genetics is viewed as the bridge between complex mathematical models of trait evolution and real-world data, and the authors have clearly framed their treatment as such. This is the second volume in a planned trilogy that summarizes the modern field of quantitative genetics, informed by empirical observations from wide-ranging fields (agriculture, evolution, ecology, and human biology) as well as population genetics, statistical theory, mathematical modeling, genetics, and genomics. Whilst volume 1 (1998) dealt with the genetics of such traits, the main focus of volume 2 is on their evolution, with a special emphasis on detecting selection (ranging from the use of genomic and historical data through to ecological field data) and examining its consequences. This extensive work of reference is suitable for graduate level students as well as professional researchers (both empiricists and theoreticians) in the fields of evolutionary biology, genetics, and genomics. It will also be of particular relevance and use to plant and animal breeders, human geneticists, and statisticians.

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Maintenance of Quantitative Genetic Variation

10.1093/oso/9780198830870.003.0028 ◽

2018 ◽

Author(s):

Bruce Walsh ◽

Michael Lynch

Keyword(s):

Genetic Variation ◽

Quantitative Genetics ◽

Stabilizing Selection ◽

Quantitative Genetic ◽

Wide Range

One of the major unresolved issues in quantitative genetics is what accounts for the amount of standing genetic variation in traits. A wide range of models, all reviewed in this chapter, have been proposed, but none fit the data, either giving too much variation or too little apparent stabilizing selection.

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Identification and classification of Asian soybean rust using leaf-based hyperspectral reflectance

International Journal of Remote Sensing ◽

10.1080/01431161.2021.1890855 ◽

2021 ◽

Vol 42 (11) ◽

pp. 4177-4198

Author(s):

Renato Herrig Furlanetto ◽

Marcos Rafael Nanni ◽

Monica Sayuri Mizuno ◽

Luís Guilherme Teixeira Crusiol ◽

Camila Rocco da Silva

Keyword(s):

Soybean Rust ◽

Hyperspectral Reflectance ◽

Asian Soybean Rust

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Rapid Determination of Minced Beef Adulteration Using Hyperspectral Reflectance Spectroscopy and Multivariate Methods

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/428/1/012049 ◽

2020 ◽

Vol 428 ◽

pp. 012049 ◽

Cited By ~ 1

Author(s):

Bingqing Guo ◽

Jinling Zhao ◽

Shizhuang Weng ◽

Xun Yin ◽

Peipei Tang

Keyword(s):

Rapid Determination ◽

Reflectance Spectroscopy ◽

Multivariate Methods ◽

Hyperspectral Reflectance ◽

Minced Beef

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Effect of leaf temperature on the estimation of photosynthetic and other traits of wheat leaves from hyperspectral reflectance

Journal of Experimental Botany ◽

10.1093/jxb/eraa514 ◽

2020 ◽

Author(s):

Hammad A Khan ◽

Yukiko Nakamura ◽

Robert T Furbank ◽

John R Evans

Keyword(s):

Leaf Traits ◽

Leaf Temperature ◽

Physiological Traits ◽

Leaf Mass Per Area ◽

Hyperspectral Reflectance ◽

Wheat Varieties ◽

Unit Leaf ◽

Temperature Signal ◽

Wheat Leaves ◽

Nitrogen Contents

Abstract A growing number of leaf traits can be estimated from hyperspectral reflectance data. These include structural and compositional traits, such as leaf mass per area (LMA) and nitrogen and chlorophyll content, but also physiological traits such a Rubisco carboxylation activity, electron transport rate, and respiration rate. Since physiological traits vary with leaf temperature, how does this impact on predictions made from reflectance measurements? We investigated this with two wheat varieties, by repeatedly measuring each leaf through a sequence of temperatures imposed by varying the air temperature in a growth room. Leaf temperatures ranging from 20 °C to 35 °C did not alter the estimated Rubisco capacity normalized to 25 °C (Vcmax25), or chlorophyll or nitrogen contents per unit leaf area. Models estimating LMA and Vcmax25/N were both slightly influenced by leaf temperature: estimated LMA increased by 0.27% °C–1 and Vcmax25/N increased by 0.46% °C–1. A model estimating Rubisco activity closely followed variation associated with leaf temperature. Reflectance spectra change with leaf temperature and therefore contain a temperature signal.

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