Spikelet abortion in six-rowed barley is mainly influenced by final spikelet number with potential spikelet number acting as a suppressor trait

Abstract The potential to increase barley grain yield lies in the indeterminate nature of its inflorescence meristem (IM). The IM produces spikelets, the basic reproductive unit in grasses, which are linked to reproductive success. During early reproductive growth, barley spikes pass through the maximum yield potential—a stage after which no new spikelet ridges are produced. Subsequently, spikelet abortion (SA), a phenomenon in which spikelets abort during spike growth, imposes a bottleneck on increasing the grain yield potential. Here, we studied the potential of main culm spikes by counting potential spikelet number (PSN), final spikelet number (FSN) and computed the corresponding SA (%) in a panel of 417 six-rowed spring barleys. Our phenotypic data analyses showed a significantly large within- and across-years genotypic variation with high broad-sense heritability estimates for all the investigated traits, including SA. Asian accessions displayed the lowest SA indicating the presence of favorable alleles that may be exploited in breeding programs. A significantly negative Pearson’s product-moment correlation was observed between FSN and SA. Our path analysis revealed that PSN and FSN explain 93% of the observed phenotypic variability for SA with PSN behaving as a suppressor trait magnifying the effect. Based on a large set of diverse barley accessions, our results provide a deeper phenotypic understanding of the quantitative genetic nature of SA, its association with traits of high agronomic importance, and a resource for further genetic analyses.

Download Full-text

Early generation selection in wheat. I. Yield potential

Australian Journal of Agricultural Research ◽

10.1071/ar9891117 ◽

1989 ◽

Vol 40 (6) ◽

pp. 1117 ◽

Cited By ~ 16

Author(s):

KJ Quail ◽

RA Fischer ◽

JT Wood

Keyword(s):

Grain Yield ◽

Plant Traits ◽

Genotypic Variation ◽

Genotype By Environment Interaction ◽

Kernel Weight ◽

Yield Potential ◽

Leaf Angle ◽

Environment Interaction ◽

Spike Number ◽

Erect Leaves

F3 single plant traits were tested as possible selection criteria for increasing yield potential. F3 plants were grown spaced in a glasshouse, while yield was measured in southern New South Wales under irrigation and optimum management. Thc population studied comprised 220 F1-derived lines taken at random from a multiple convergent cross amongst 16 parents representing elite CTMMYT germplasm of the mid 1970s but containing diversity for major dwarfing genes, maturity, leaf angle and other traits. More than 50 traits were determined, comprising numerical components of yield, size and morphology, partitioning ratios, development rates and physiological activities. All F3 traits showed significant genotypic variation which was usually greater for progeny lines than for parents although only occasionally significantly so. Broad sense heritability was generally moderate to high.F3 lines were advanced by single seed descent for replicated F7 and F8 yield experiments, two in each of 1982 and 1983. In each experiment 60-68 progeny lines chosen at random were tested; 44 lines were common to all experiments. Plot size was 8 rows X 5 m, and edge rows and plot ends were discarded. Yield levels were high (mean yield 5.9 t h a 1 at 10Yo moisture) and largely free of interference from lodging and disease. The progeny main effect on grain yield was highly significant, but no progeny line significantly outyielded the best parent. Best correlations with progeny grain yield were given by F3 plant height (r= -0.31 to -0.50 across experiments), F3 kernel weight (r= -0.03 to -0.44), F3 harvest index (r = 0.18 to 0.5 l), F3 leaf angle (r = -0.13 to -0.40, erect leaves favouring high yicld) and F3 spike number (r=0.08 to 0.40). Retrospective selection in F3 using these traits singly at a selection intensity of 25% gave increases in population mean yield (0 to + 12%) and in the proportion of high yielding lines (doubled in some cases), but only selection in F3 for reduced stature is considered worthwhile for advancing yield potential. It is suggested that the ineffectiveness of F3 selection is largely due to genotype by environment interaction, along with the complex multigenic nature of grain yicld.

Download Full-text

Strategies of grain number determination differentiate barley row types

Journal of Experimental Botany ◽

10.1093/jxb/erab395 ◽

2021 ◽

Author(s):

Venkatasubbu Thirulogachandar ◽

Ravi Koppolu ◽

Thorsten Schnurbusch

Keyword(s):

Yield Components ◽

Growth Conditions ◽

Barley Grain ◽

Yield Potential ◽

Spikelet Number ◽

Grain Number ◽

Fundamental Interactions ◽

The Stability

Abstract Gaining knowledge on fundamental interactions of various yield components is crucial to improve yield potential in small grain cereals. It is well known in barley that increasing grain number greatly improves yield potential; however, the yield components determining grain number and their association in barley row types are less explored. In this study, we assessed different yield components such as potential spikelet number (PSN), spikelet survival (SSL), spikelet number (SN), grain set (GS), and grain survival (GSL), as well as their interactions with grain number 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 the greenhouse and the field. From this study, we found that in two-rowed barley, grain number determination is strongly influenced by PSN rather than SSL and/or GS in both growth conditions. Conversely, in six-rowed barley, grain number is associated with SSL instead of PSN and/or GS. Thus, our study showed that increasing grain number might be possible by augmenting PSN in two-rowed genotypes, while for six-rowed genotypes SSL needs to be improved. We speculate that this disparity of grain number determination in barley row types might be due to the fertility of lateral spikelets. Collectively, this study revealed that grain number in two-rowed barley largely depends on the developmental trait, PSN, while in six-rowed barley, it mainly follows the ability for SSL.

Download Full-text

Interaksi Genotipe x Lingkungan untuk Hasil Gabah Padi Sawah

Jurnal Penelitian Pertanian Tanaman Pangan ◽

10.21082/jpptp.v35n2.2016.p89-98 ◽

2016 ◽

Vol 35 (2) ◽

pp. 89

Author(s):

Trias Sitaresmi ◽

Cucu Gunarsih ◽

Nafisah Nafisah ◽

Yudhistira Nugraha ◽

Buang Abdullah ◽

...

Keyword(s):

Grain Yield ◽

Maximum Yield ◽

Yield Potential ◽

Environment Interaction ◽

Wet Season ◽

Genotype X Environment ◽

Experiment Unit ◽

Check Variety ◽

Rice Genotypes ◽

High Degree

Grain yield of rice is determined by genotype (G), environment (E), and interaction between genotype x environment (G x E). Variety can achieve its maximum yield potential if it is grown in suitable environments. This study was aimed to determine the adaptability and the yield stability of rice genotypes grown in different environments. Sixteen rice genotypes were tested using RBD in 16 sites during the wet season of 2010/2011, and dry season of 2011. The tested rice lines were developed for resistance to pest and diseases. The experiment unit was 4 m x 5 m of plot, plants were fertilized with urea, SP36, and KCl at rates of 250 kg/ha, 100 kg/ha, and 100 kg/ha, respectively. Variable observed was grain yield per plot. Combined analyses of variance showed that there was no lines yielded higher than did check variety Conde. The AMMI analysis showed that the largest variation was contributed by the environment factors (76.49%), genotype x environment interactions (17.55%), and the smallest was contributed by the genotypes (5.97%). Data exploration using boxplot method indicated that the low contribution of the genotype x environment interaction variance in this study was due to the high degree of similarity of yield potentials among the genotypes, and due to high similarity of environmental conditions of the sites.Based on the analysis of AMMI 2, lines B12743 - MR-18-2-3-8, IPB107-F-82-2-1, and Conde was each classified as widely adapted genotypes, while G8, IPB107-F-27-6-1, and BIO111-2-BC-PIR-3714, each was considered as genotype having a specific adaptation.

Download Full-text

Agronomic performance of grain sorghum in different spatial arrangements

Semina Ciências Agrárias ◽

10.5433/1679-0359.2020v41n4p1107 ◽

2020 ◽

Vol 41 (4) ◽

pp. 1107

Author(s):

Antonio Germano Carpim Rocha ◽

Eduardo Lima do Carmo ◽

Guilherme Braga Pereira Braz ◽

Luiz Fernando Ribeiro Júnior ◽

Carlos César Evangelista de Menezes ◽

...

Keyword(s):

Grain Yield ◽

Field Experiments ◽

Plant Density ◽

Block Design ◽

Maximum Yield ◽

Morphological Characteristics ◽

Grain Sorghum ◽

Plant Distribution ◽

Yield Potential ◽

Agronomic Performance

Grain sorghum has been cultivated extensively in the Central-West region of Brazil in succession with soybean, since the species tolerates the low rainfall conditions recorded during this growing season. Phytotechnological adjustments for sorghum are still necessary in order to exploit the maximum yield potential of the crop. In this context, altering the plant distribution may result in better utilization of the growing area with a consequent increase in grain yield. On this basis, field experiments were conducted with two grain sorghum hybrids, replicated in two different locations, in order to evaluate the effect of the spatial arrangement of plants on the agronomic performance of grain sorghum. The design was a randomized complete block design in a 2 x 4 factorial scheme, with five replications. The factors consisted of two row spacings (0.50 and 0.25 m) and four plant densities: 120, 180, 240, and 300 thousand plants ha-1. The sorghum hybrids used were 1G244® and 1G100®. At harvest, morphological characteristics, yield components, and grain yields were evaluated. Row spacing did not influence the characteristics evaluated except for the plant height of for both hybrids, in addition to the stem diameter, tillering, and mass of a thousand grains for 1G100®. The increase in plant density provided an increase in grain yield, being the highest number of panicles per area the most expressive component for this behavior.

Download Full-text

Identification of QTL-s for drought tolerance in maize, II: Yield and yield components

Genetika ◽

10.2298/gensr1302341n ◽

2013 ◽

Vol 45 (2) ◽

pp. 341-350 ◽

Cited By ~ 9

Author(s):

Ana Nikolic ◽

Violeta Andjelkovic ◽

Dejan Dodig ◽

Snezana Mladenovic-Drinic ◽

Natalija Kravic ◽

...

Keyword(s):

Drought Tolerance ◽

Grain Yield ◽

Yield Components ◽

Phenotypic Variability ◽

Interval Mapping ◽

Field Trials ◽

Chromosome 9 ◽

Yield Potential ◽

Maize Breeding ◽

Yield And Yield Components

Grain yield is the primary trait of interest in maize breeding programs. Worldwide, drought is the most pervasive limitation to the achievement of yield potential in maize. Drought tolerance of maize has been considerably improved through conventional breeding. Traditional breeding methods have numerous limitations, so development of new molecular genetics techniques could help in elucidation of genetic basis of drought tolerance .In order to map QTLs underlying yield and yield components under drought 116 F3 families of DTP79xB73 cross were evaluated in the field trials. Phenotypic correlations calculated using Pearson?s coefficients were high and significant. QTL detection was performed using composite interval mapping option in WinQTL Cartographer v 2.5. Over all nine traits 45 QTLs were detected: five for grain yield per plant and 40 for eight yield components. These QTLs were distributed on all chromosomes except on chromosome 9. Percent of phenotypic variability determined for the identified QTLs for all the traits was in the range from 27.46 to 95.85%. Different types of gene action were found for the QTLs identified for analyzed traits.

Download Full-text

Genotype by environment interaction, correlation, AMMI, GGE biplot and cluster analysis for grain yield and other agronomic traits in sorghum (Sorghum bicolor L. Moench)

PLoS ONE ◽

10.1371/journal.pone.0258211 ◽

2021 ◽

Vol 16 (10) ◽

pp. e0258211

Author(s):

Muluken Enyew ◽

Tileye Feyissa ◽

Mulatu Geleta ◽

Kassahun Tesfaye ◽

Cecilia Hammenhag ◽

...

Keyword(s):

Grain Yield ◽

Plant Height ◽

Agronomic Traits ◽

Genotypic Variation ◽

Genotype By Environment Interaction ◽

Environment Interaction ◽

Phenotypic Data ◽

Breeding Programs ◽

Genotype By Environment ◽

And Cluster Analysis

Genotype by environment (G×E) interaction is a major factor limiting the success of germplasm selection and identification of superior genotypes for use in plant breeding programs. Similar to the case in other crops, G×E complicates the improvement of sorghum, and hence it should be determined and used in decision-making programs. The present study aimed at assessing the G×E interaction, and the correlation between traits for superior sorghum genotypes. Three hundred twenty sorghum landraces and four improved varieties were used in alpha lattice experimental design-based field trial across three environments (Melkassa, Mieso and Mehoni) in Ethiopia. Phenotypic data were collected for days to flowering (DTF), plant height (PH), panicle length (PALH), panicle width (PAWD), panicle weight (PAWT) and grain yield (GY). The results revealed that the variance due to genotype, environment and G×E interaction were highly significant (P < 0.001) for all traits. GY and PAWT were highly affected by environments and G×E whereas DTF, PALH, PAWD and PH were mainly affected by genotypic variation. Therefore, multi-environment testing is needed for taking care of G × E interaction to identify high yielding and stable sorghum landraces. GY and PAWT revealed highly significant positive correlations indicating the possibility of effective selection of the two traits simultaneously. Among the studied populations, South Wello, West Hararghe and Shewa zones had highly diverse genotypes that were distributed across all clusters. Hence, these areas can be considered as hotspots for identifying divergent sorghum landraces that could be used in breeding programs. Melkassa was the most representative environment whereas Mieso was the most discriminating. Five genotypes (G148, G123, G110, G203 and G73) were identified as superior across the test environments for grain yield with farmer-preferred trait, such as plant height. The identified stable and high yielding genotypes are valuable genetic resources that should be used in sorghum breeding programs.

Download Full-text

Agronomic Management and Interventions to Increase Rice Yield in Bangladesh

Bangladesh Rice Journal ◽

10.3329/brj.v24i2.53453 ◽

2021 ◽

Vol 24 (2) ◽

pp. 161-181

Author(s):

MKA Bhuiyan ◽

AKMS Islam ◽

MAR Sarkar ◽

MAA Mamun ◽

MU Salam ◽

...

Keyword(s):

Grain Yield ◽

Weed Management ◽

Crop Production ◽

Rice Yield ◽

Flash Flood ◽

Proper Time ◽

Maximum Yield ◽

Leaf Sheath ◽

Spikelet Number ◽

Agronomic Management

Avenues of agronomic manipulation need to be explored critically for getting potential rice yield in a given environment. Increasing population, decreasing resources and increasing climate vulnerability such as salinity, drought, submergence, early flash flood in haor areas can interrupt achieving the target of rice yield. Location specific variety, profitable cropping sequences, innovative and smart cultural management, and appropriate agronomic management with smart dissemination using multiple means would maximize rice yield and decrease the production barriers of rice. Appropriate variety and location- specific crop management systems should be formulated for rice yield maximization to reduce yield gap in farmers’ field. A number of approaches can be undertaken for maximizing rice yield by adopting location-specific crop production e.g., manipulating sowing and planting times, appropriate weed management technology in proper time, suitable variety selection for improving cropping intensity, Judicious and balance application of organic and inorganic fertilizer application etc. Nitrogen application before panicle primordia is crucial because at this stage panicle primordia determined the spikelet number of the panicle and the absorbed nitrogen is efficiently used to increase spikelet number, accumulated photosynthates to leaf sheath and culm and, hence, increases panicle size and grain yield. Farmers should have a plan and should follow different steps of rice production to get higher yield and sustain productivity. Rice growth stage- wise agronomic management should be followed to get maximum yield. Choice of appropriate variety in a specific location or ecosystem is a major concern that contributed about 20% to the grain yield. Whereas management is a big issues which contributed about 60% for obtaining higher grain yield.The difference of environment × management explained the largest variations ( 80%) in explaining the yield. The bridging of knowledge gaps can bridge yield gaps. New paradigms need to be added to transfer and use new high yielding varieties and knowledge based technologies under new policy settings. Bangladesh Rice J. 24 (2): 161-181, 2021

Download Full-text

Variable-rate in corn sowing for maximizing grain yield

Scientific Reports ◽

10.1038/s41598-021-92238-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Eder Eujácio da Silva ◽

Fábio Henrique Rojo Baio ◽

Daniel Fernando Kolling ◽

Renato Schneider Júnior ◽

Alex Rogers Aguiar Zanin ◽

...

Keyword(s):

Grain Yield ◽

Vegetative Growth ◽

Maximum Yield ◽

Influential Factors ◽

Plant Population ◽

Seeding Density ◽

Corn Yield ◽

Growth Indices ◽

Experimental Conditions ◽

Soil Attributes

AbstractSowing density is one of the most influential factors affecting corn yield. Here, we tested the hypothesis that, according to soil attributes, maximum corn productivity can be attained by varying the seed population. Specifically, our objectives were to identify the soil attributes that affect grain yield, in order to generate a model to define the optimum sowing rate as a function of the attributes identified, and determine which vegetative growth indices can be used to predict yield most accurately. The experiment was conducted in Chapadão do Céu-GO in 2018 and 2019 at two different locations. Corn was sown as the second crop after the soybean harvest. The hybrids used were AG 8700 PRO3 and FS 401 PW, which have similar characteristics and an average 135-day cropping cycle. Tested sowing rates were 50, 55, 60, and 65 thousand seeds ha−1. Soil attributes evaluated included pH, calcium, magnesium, phosphorus, potassium, organic matter, clay content, cation exchange capacity, and base saturation. Additionally, we measured the correlation between the different vegetative growth indices and yield. Linear correlations were obtained through Pearson’s correlation network, followed by path analysis for the selection of cause and effect variables, which formed the decision trees to estimate yield and seeding density. Magnesium and apparent electrical conductivity (ECa) were the most important soil attributes for determining sowing density. Thus, the plant population should be 56,000 plants ha−1 to attain maximum yield at ECa values > 7.44 mS m−1. In addition, the plant population should be 64,800 plants ha−1 at values < 7.44 mS m−1 when magnesium levels are greater than 0.13 g kg−1, and 57,210 plants ha−1 when magnesium content is lower. Trial validation showed that the decision tree effectively predicted optimum plant population under the local experimental conditions, where yield did not significantly differ among populations.

Download Full-text

Contribution of Stem Dry Matter to Grain Yield in Wheat Cultivars

Functional Plant Biology ◽

10.1071/pp9910053 ◽

1991 ◽

Vol 18 (1) ◽

pp. 53 ◽

Cited By ~ 51

Author(s):

PC Pheloung ◽

KHM Siddique

Keyword(s):

Water Stress ◽

Grain Yield ◽

Dry Matter ◽

Field Experiments ◽

Yield Potential ◽

Percentage Reduction ◽

Wheat Cultivars ◽

Activity Increase ◽

Structural Carbohydrate ◽

Days After Anthesis

Field experiments were conducted in the eastern wheat belt of Western Australia in a dry year with and without irrigation (1987) and in a wet year (1988), comparing three cultivars of wheat differing in height and yield potential. The aim of the study was to determine the contribution of remobilisable stem dry matter to grain dry matter under different water regimes in old and modern wheats. Stem non-structural carbohydrate was labelled with 14C 1 day after anthesis and the activity and weight of this pool and the grain was measured at 2, 18 and 58 days after anthesis. Gutha and Kulin, modern tall and semi-dwarf cultivars respectively, yielded higher than Gamenya, a tall older cultivar in all conditions, but the percentage reduction in yield under water stress was greater for the modern cultivars (41, 34 and 23%). In the grain of Gamenya, the increase in 14C activity after the initial labelling was highest under water stress. Generally, loss of 14C activity from the non-structural stem dry matter was less than the increase in grain activity under water stress but similar to or greater than grain activity increase under well watered conditions. Averaged over environments and cultivars, non-structural dry matter stored in the stem contributed at least 20% of the grain dry matter.

Download Full-text