Raw data for: Modes of grain number determination differentiate barely row-types
Thirulogachandar, Venkatasubbbu; Koppolu, Ravi; Schnurbusch, Thorsten (2021), Raw data for: Modes of grain number determination differentiate barely row-types, Dryad, Dataset, https://doi.org/10.5061/dryad.c866t1g4z
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.
Plants and growth conditions
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, 20o C/16o C, light/dark) for two weeks. Plants were vernalized at 4o 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, 20o C/16o 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.
Phenotyping traits and measurement
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).
Spikelet survival=Final spikelet number Potential spikelet number × 100------ 1
Grain survival=Grain numberPotential spikelet number × 100-------- (2)
Grain set=Grain numberFinal spikelet number × 100-------- (3)
Methods of data analysis
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).
European Research Council, Award: 681686 “LUSH SPIKE,” ERC-2015-CoG