DOI: 10.5061/dryad.c866t1g4z
title: Raw data for: Modes of grain number determination differentiate barely row-types

description: Gaining knowledge on intrinsic interactions of various yield components is
 crucial to improve the yield potential in small grain cereals. It is well
 known in barley that increasing the grain number (GN) preponderantly
 improves their yield potential; however, the yield components determining
 GN and their association in barley row-types are less explored. In this
 study, we assessed different yield components like potential spikelet
 number (PSN), spikelet survival (SSL), spikelet number (SN), grain set
 (GS), and grain survival (GSL), as well as their interactions with GN by
 using a selected panel of two- and six-rowed barley types. Also, to
 analyze the stability of these interactions, we performed the study in two
 growth conditions, greenhouse and field. From this study, we found that in
 two-rowed, GN determination is strongly influenced by PSN rather than SSL
 and/or GS in both growth conditions. Conversely, in six-rowed, GN is
 associated with SSL instead of PSN and/or GS. Thus, our study exemplified
 that increasing GN might be possible by augmenting PSN in two-rowed
 genotypes, while for six-rowed genotypes, the ability of SSL needs to be
 improved. We speculate that this disparity of GN determination in barley
 row-types might be due to the fertility of lateral spikelets.
 Collectively, this study revealed that GN of two-rowed largely depends on
 the developmental trait, PSN, while in six-rowed, it mainly follows the
 ability of SSL.
description: <b>Plants and growth
 conditions</b> The study was carried out at the
 Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben,
 Germany (51° 49′ 23″ N, 11° 17′ 13″ E, altitude 112 m) during the 2017
 growing season. Twenty-seven barley accessions (Supplementary Table 1)
 were selected from a worldwide collection panel (Alqudah et al., 2014)
 based on their variation in spike developmental traits. The selected panel
 was used for two experiments conducted in the field (March to August 2017)
 and greenhouse (July to December 2017). For both the experiments, similar
 sowing and pre-treatment protocols were followed. Barley grains were sown
 on 96 well trays and grown under greenhouse condition (photoperiod,
 16h/8h, light/dark; temperature, 20<sup>o
 </sup>C/16<sup>o </sup>C, light/dark) for two weeks.
 Plants were vernalized at 4<sup>o </sup>C for four weeks and
 then acclimatized in the greenhouse for a week. Following the hardening
 process, plants were directly transplanted in the silty loam soil (5
 plants per 1.2 m long rows with 20 cm distance between the rows) for the
 field experiment. In the greenhouse experiment (photoperiod, 16h/8h,
 light/dark; temperature, 20<sup>o </sup>C/16<sup>o
 </sup>C, light/dark), plants were potted in a 9 cm pot (9 × 9 cm,
 diameter × height) with two parts of autoclaved compost, two parts of
 ‘Rotes Substrat’  (Klasmann-Deilmann GmbH, Germany), and one part of sand.
 Standard practices for irrigation, fertilization, and control of pests and
 diseases were followed. The temperature, light, and other environmental
 conditions of the field and greenhouse experiments are given in
 supplementary table 2. <b>Phenotyping traits and
 measurement</b> All the traits reported in this
 study were measured from the main culm of a barley plant because it is
 less influenced by environmental perturbations and growth conditions. The
 direct yield component traits like potential spikelet number (PSN), final
 spikelet number (SN), and grain number (GN) were collected from randomly
 selected plants at two stages, maximum yield potential (MYP) and harvest.
 PSN was counted at the MYP stage, while SN and GN were enumerated at the
 harvest. At the MYP stage, data were collected from three random plants
 for both the experiments, whereas at harvest, six plants were used in the
 field and twelve plants in the greenhouse experiment. The MYP stage was
 identified by tracking the development of spikes based on the
 developmental scales previously been used (Waddington, 1983; Kirby and
 Appleyard, 1984). The stage at which the initiation of spikelet primordia
 stops at the apex of the main shoot spike was considered as the MYP
 stage. All the
 initiated spikelet primordia and differentiated spikelets were counted at
 the MYP stage and regarded as potential spikelet number (PSN) of a spike.
 At MYP, more apically localized spikelet primordia ridges in the double
 ridge stage were multiplied by three since barley spikes form three
 spikelet primordia at a spike axis (rachis) node (Waddington, 1983; Kirby
 and Appleyard, 1984). The final spikelet and GN were counted from a spike
 after the physiological maturity of the plant. All (assimilate-) filled
 and unfilled spikelets were counted as final spikelet number, while only
 the filled spikelets regarded as grains. Spikelet and grain survival
 traits were calculated from the equations (1) and (2), respectively, and
 grain set from the equation (3). <i>Spikelet
 survival=</i><i>Final spikelet number
 </i><i>Potential spikelet number </i><i> ×
 100------ </i><i>1</i>   <i>Grain survival=</i><i>Grain number</i><i>Potential spikelet number</i><i> × 100-------- (2)</i>   <i>Grain set=</i><i>Grain number</i><i>Final spikelet number</i><i> × 100-------- (3)</i>
description: <b>Methods of data
 analysis</b> All the data analyzes were done
 in the Prism software, version 8.4.2 (GraphPad Software, LLC), except the
 non-linear quadratic (squared) fit. The outliers were identified by the
 ‘ROUT’ method described previously (Motulsky and Brown, 2006). The mean
 value comparison between the environments for every genotype and row-types
 was analyzed by the multiple t-tests and paired t-test (parametric),
 respectively. Also, the false discovery rate approach of the two-stage
 linear step-up procedure of Benjamini, Kreier, and Yekutieli with the Q
 value of 5% was applied to calculate the significance of t-tests. Only for
 PSN and GS, a two-way ANOVA with Tukey’s multiple comparison test
 (alpha=5%) was conducted. The replicate values of a genotype and the
 row-types were analyzed individually, without assuming a consistent
 standard deviation. Linear regression was done by using the appropriate
 dependent (Y values) and independent (X values) traits. The 95% confidence
 intervals were identified for every linear regression and plotted as
 confidence bands along with the ‘goodness of fit’ line. Non-linear
 quadratic (squared) fit for selected dependent and independent traits was
 done with the data analysis tool kit of Microsoft office-Excel (version
 2016).