Literature DB >> 32534622

Remote sensing techniques and stable isotopes as phenotyping tools to assess wheat yield performance: Effects of growing temperature and vernalization.

Fatima Zahra Rezzouk1, Adrian Gracia-Romero1, Shawn C Kefauver1, Nieves Aparicio Gutiérrez2, Iker Aranjuelo3, Maria Dolors Serret1, José Luis Araus4.   

Abstract

This study compares distinct phenotypic approaches to assess wheat performance under different growing temperatures and vernalization needs. A set of 38 (winter and facultative) wheat cultivars were planted in Valladolid (Spain) under irrigation and two contrasting planting dates: normal (late autumn), and late (late winter). The late plating trial exhibited a 1.5 °C increase in average crop temperature. Measurements with different remote sensing techniques were performed at heading and grain filling, as well as carbon isotope composition (δ13C) and nitrogen content analysis. Multispectral and RGB vegetation indices and canopy temperature related better to grain yield (GY) across the whole set of genotypes in the normal compared with the late planting, with indices (such as the RGB indices Hue, a* and the spectral indices NDVI, EVI and CCI) measured at grain filling performing the best. Aerially assessed remote sensing indices only performed better than ground-acquired ones at heading. Nitrogen content and δ13C correlated with GY at both planting dates. Correlations within winter and facultative genotypes were much weaker, particularly in the facultative subset. For both planting dates, the best GY prediction models were achieved when combining remote sensing indices with δ13C and nitrogen of mature grains. Implications for phenotyping in the context of increasing temperatures are further discussed.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  High temperature; Remote sensing; Stable isotopes; Vernalization; Wheat; Yield prediction

Year:  2019        PMID: 32534622     DOI: 10.1016/j.plantsci.2019.110281

Source DB:  PubMed          Journal:  Plant Sci        ISSN: 0168-9452            Impact factor:   4.729


  2 in total

1.  Accuracy Assessment of Kriging, artificial neural network, and a hybrid approach integrating spatial and terrain data in estimating and mapping of soil organic carbon.

Authors:  Miraç Kılıç; Recep Gündoğan; Hikmet Günal; Bilal Cemek
Journal:  PLoS One       Date:  2022-05-26       Impact factor: 3.752

2.  Comparative Performance of High-Yielding European Wheat Cultivars Under Contrasting Mediterranean Conditions.

Authors:  Valter Jário de Lima; Adrian Gracia-Romero; Fatima Zahra Rezzouk; Maria Carmen Diez-Fraile; Ismael Araus-Gonzalez; Samuel Henrique Kamphorst; Antonio Teixeira do Amaral Júnior; Shawn C Kefauver; Nieves Aparicio; Jose Luis Araus
Journal:  Front Plant Sci       Date:  2021-06-29       Impact factor: 5.753

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.