DOI: 10.5061/dryad.xwdbrv1cv
title: Strategies 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 the 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 (April 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 conditions (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 for the
 field experiment. The field experiment was a single plot per genotype, and
 each plot had eight rows with a 15 cm distance between the rows. Each row
 was 0.8 m long with five plants per row. 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. We conducted a third experiment in
 the field (same location mentioned above) named ‘Random culm-field’
 between April 2019 to August 2019. In this experiment, the 27 genotypes
 were replicated in three random block model, in which a block size was 1 ×
 1.5 m<sup>2</sup>, and in every block, a genotype was sown
 with a density of 300 grains/m<sup>2</sup>. Each block had six
 rows spaced 20 cm between them. Standard practices for irrigation,
 fertilization, and control of pests and diseases were followed. The
 temperature, light, and other environmental conditions of the field,
 greenhouse, and random culm-field experiments are given in supplementary
 table 2. <b>Phenotyping traits and
 measurement</b> All the traits reported from the
 field and greenhouse studies (the year 2017) were measured from the main
 culm of a barley plant because of its higher contribution to the final
 grain yield and it is less influenced by environmental perturbations and
 growth conditions (Cottrell et al., 1985; Elhani et al., 2007). 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. However, in the
 random culm-field experiment, all the parameters were collected from three
 random culms of every block, which made nine replications per genotype.
 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 SN and GN were counted from a spike after
 the physiological maturity of the plant. All (assimilate-) filled and
 unfilled spikelets were counted as final SN, while only the filled
 spikelets were regarded as grains. Spikelet and grain survival traits were
 calculated from the equations (1) and (2), respectively, and grain set
 from 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> <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. 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 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).
description:  The raw data* file contains four sheets of data that
 belong to the manuscript "Strategies of grain number determination
 differentiate barley row-types."       The first
 sheet has the data of Potential spikelet number (PSN), the second sheet
 has Grain number (GN), the third sheet has Spikelet number (SN), and the
 fourth sheet has Grain set (GS)       Preprocessing
 of the data is described in the section on materials and methods.  
     *Unprocessed data; it may require the removal of
 outliers by an appropriate method.