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      Dataset of above and below ground traits assessed in Durum wheat cultivars grown under Mediterranean environments differing in water and temperature conditions

      data-paper
      a , b , a , b , a , b , c , a , b , a , b , *
      Data in Brief
      Elsevier
      Stable isotopes, Root traits, Leaf pigments, Canopy temperature, CT, canopy temperature, DTH, days to heading, HI, harvest index, ILP, irrigated late planting, INP, irrigated normal planting, GN, grain number, GNY, total grain nitrogen yield, GY, grain yield, NBI, nitrogen balance index, NDVI, normalized difference vegetation index, PCA, principle component analysis, PH, plant height, RA, root angle, RNP, rainfed normal planting, SRL, Specific root length, TGW, thousand grain weight, δ13C, carbon isotope composition, δ15N, nitrogen isotope composition, δ18O, oxygen isotope composition

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          Abstract

          Ideotypic characteristics of durum wheat associated with higher yield under different water and temperature regimes were studied under Mediterranean conditions. The focus of this paper is to provide raw and supplemental data from the research article entitled “Durum wheat ideotypes in Mediterranean environments differing in water and temperature conditions” [1], which aims to define specific durum wheat ideotypes according to their responses to different agronomic conditions. In this context, six modern (i.e. post green revolution) genotypes with contrasting yield performance (i.e. high vs low yield) were grown during two consecutive years under different treatments: (i) winter planting under support-irrigation conditions, (ii) winter planting under rainfed conditions, (iii) late planting under support-irrigation. Trials were conducted at the INIA station of Colmenar de Oreja (Madrid). Different traits were assessed to inform about water status (canopy temperature at anthesis and stable carbon isotope composition (δ 13C) of the flag leaf and mature grains), root performance (root traits and the oxygen isotope composition (δ 18O) in the stem base water), phenology (days from sowing to heading), nitrogen status/photosynthetic capacity (nitrogen content and stable isotope composition (δ 15N) of the flag leaf and mature grain together with the pigment contents and the nitrogen balance index (NBI) of the flag leaf), crop growth (plant height (PH) and the normalized difference vegetation index (NDVI) at anthesis), grain yield and agronomic yield components. For most of the parameters assessed, data analysis demonstrated significant differences among genotypes within each treatment. The level of significance was determined using the Tukey-b test on independent samples, and ideotypes were modelled from the results of principle component analysis. The present data shed light on traits that help to define specific ideotype characteristics that confer genotypic adaptation to a wide range of agronomic conditions produced by variations in planting date, water conditions and season.

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          GiA Roots: software for the high throughput analysis of plant root system architecture

          Background Characterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks. Results We have developed GiA Roots (General Image Analysis of Roots), a semi-automated software tool designed specifically for the high-throughput analysis of root system images. GiA Roots includes user-assisted algorithms to distinguish root from background and a fully automated pipeline that extracts dozens of root system phenotypes. Quantitative information on each phenotype, along with intermediate steps for full reproducibility, is returned to the end-user for downstream analysis. GiA Roots has a GUI front end and a command-line interface for interweaving the software into large-scale workflows. GiA Roots can also be extended to estimate novel phenotypes specified by the end-user. Conclusions We demonstrate the use of GiA Roots on a set of 2393 images of rice roots representing 12 genotypes from the species Oryza sativa. We validate trait measurements against prior analyses of this image set that demonstrated that RSA traits are likely heritable and associated with genotypic differences. Moreover, we demonstrate that GiA Roots is extensible and an end-user can add functionality so that GiA Roots can estimate novel RSA traits. In summary, we show that the software can function as an efficient tool as part of a workflow to move from large numbers of root images to downstream analysis.
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            A new optical leaf-clip meter for simultaneous non-destructive assessment of leaf chlorophyll and epidermal flavonoids

            We have characterized a new commercial chlorophyll (Chl) and flavonoid (Flav) meter called Dualex 4 Scientific (Dx4). We compared this device to two other Chl meters, the SPAD-502 and the CCM-200. In addition, Dx4 was compared to the leaf-clip Dualex 3 that measures only epidermal Flav. Dx4 is factory-calibrated to provide a linear response to increasing leaf Chl content in units of µg cm–2, as opposed to both SPAD-502 and CCM-200 that have a non-linear response to leaf Chl content. Our comparative calibration by Chl extraction confirmed these responses. It seems that the linear response of Dx4 derives from the use of 710 nm as the sampling wavelength for transmittance. The major advantage of Dx4 is its simultaneous assessment of Chl and Flav on the same leaf spot. This allows the generation of the nitrogen balance index (NBI) used for crop surveys and nitrogen nutrition management. The Dx4 leaf clip, that incorporates a GPS receiver, can be useful for non-destructive estimation of leaf Chl and Flav contents for ecophysiological research and ground truthing of remote sensing of vegetation. In this work, we also propose a consensus equation for the transformation of SPAD units into leaf Chl content, for general use.
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              Prioritizing quantitative trait loci for root system architecture in tetraploid wheat

              Highlight The genetic variation of root system architecture in the A and B wheat genomes is described, providing the necessary knowledge ultimately to fine-tune the expression of the root system architecture.
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                Author and article information

                Contributors
                @AGraciaRomero
                @doctorxkefauver
                Journal
                Data Brief
                Data Brief
                Data in Brief
                Elsevier
                2352-3409
                24 December 2021
                February 2022
                24 December 2021
                : 40
                : 107754
                Affiliations
                [a ]Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain
                [b ]AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198 Lleida, Spain
                [c ]Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Ctra. de La Coruña Km. 7,5, 28040 Madrid, Spain
                Author notes
                [* ]Corresponding author. jaraus@ 123456ub.edu
                Article
                S2352-3409(21)01029-5 107754
                10.1016/j.dib.2021.107754
                8718713
                35005145
                cefda1c7-cc07-473a-93f6-8393c2a838e7
                © 2021 Published by Elsevier Inc.

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 29 October 2021
                : 21 December 2021
                : 22 December 2021
                Categories
                Data Article

                stable isotopes,root traits,leaf pigments,canopy temperature,ct, canopy temperature,dth, days to heading,hi, harvest index,ilp, irrigated late planting,inp, irrigated normal planting,gn, grain number,gny, total grain nitrogen yield,gy, grain yield,nbi, nitrogen balance index,ndvi, normalized difference vegetation index,pca, principle component analysis,ph, plant height,ra, root angle,rnp, rainfed normal planting,srl, specific root length,tgw, thousand grain weight,δ13c, carbon isotope composition,δ15n, nitrogen isotope composition,δ18o, oxygen isotope composition

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