Characterizing Selected Sorghum Grain Varieties and Evaluating the Suitability of Their Malt Extracts for Cultivating Microbial Biomass

Microbial biomass is cultivated for different technological applications including food processing, medicine, waste management, and research. The conventional growth media used are generally expensive thus necessitating the development of more affordable alternatives. In this study, four sorghum grain varieties, SESO 1, SESO 3, Epuripur, and Eyera, and their malt extracts were characterized which is aimed at determining their suitability for growing microbial biomass. The varieties had kernel length, kernel width, kernel thickness, and thousand kernel weigh equivalent to 3.8-4.3 mm, 3.2-4.5 mm, 2.4-2.8 mm, and 12.4-20.2 g, respectively. SESO 1 and Epuripur had corneous endosperm textures whereas those of SESO 3 and Eyera were intermediate and floury, respectively. Varieties had germinative energies > 90 % and total defects < 8 %. SESO 3 had the highest ( p < 0.05 ) crude protein ( 10.8 ± 0.3 %) and dietary fiber ( 22.5 ± 0.4 %) whereas Epuripur had the highest ( p < 0.05 ) starch ( 81.6 ± 0.0 %) and crude fat ( 2.9 ± 0.1 %). There was no significant difference ( p > 0.05 ) in the ash contents ( 2.1 ± 0.0 %). The total sugars, free amino nitrogen, condensed tannins, and pH of the malt extracts were 106-116 g/L, 70-78 mg/L, 0.1-0.6 mg/mL, and 5.5-5.7, respectively. The composition of the sorghum malt extracts suggests their potential for use in cultivating microbial biomass.

Spike Density Quantitative Trait Loci Detection and Analysis in Tetraploid and Hexaploid Wheat Recombinant Inbred Line Populations

Spike density (SD) is an agronomically important character in wheat. In addition, an optimized spike structure is a key basis for high yields. Identification of quantitative trait loci (QTL) for SD has provided a genetic basis for constructing ideal spike morphologies in wheat. In this study, two recombinant inbred line (RIL) populations (tetraploid RIL AM and hexaploid RIL 20828/SY95-71 (2SY)) previously genotyped using the wheat55K SNP array were used to identify SD QTL. A total of 18 QTL were detected, and three were major and one was stably expressed (QSd.sau-2SY-7A.2, QSd.sau-AM-5A.2, QSd.sau-AM-7B, and QSd.sau-2SY-2D). They can explain up to 23.14, 19.97, 12.00, and 9.44% of phenotypic variation, respectively. QTL × environment and epistatic interactions for SD were further analyzed. In addition, pyramiding analysis further revealed that there were additive effects between QSd.sau-2SY-2D and QSd.sau-2SY-7A.2 in 2SY, and QSd.sau-AM-5A.2 and QSd.sau-AM-7B in AM. Pearson’s correlation between SD and other agronomic traits, and effects of major or stable QTL on yield related traits indicated SD significantly impacted spike length (SL), spikelet number per spike (SNS) and kernel length (KL). Several genes related to spike development within the physical intervals of major or stable QTL were predicted and discussed. Collectively, our research identified QTL with potential applications for modern wheat breeding and broadening the genetic basis of SD.

Study of Coefficient of Variation, Heritability and Genetic Advance for Different Traits of Rice Genotypes Grown under Aerobic Condition

The field experiments with thirty genotypes were conducted during June to October month of kharif, 2018 and kharif, 2019, to assess extent of variability under aerobic condition. The genotypes were sown under dry direct seeded condition using randomized block design (RBD) with three replications. Each genotype was sown in single row of 5 m length with spacing of 20 cm between rows and 15 cm between plants. Data recorded for 22 characters including different morphological and quality traits from 5 randomly selected plants of each replication and mean data used for analysis. ANOVA revealed that the mean sum of squares were highly significant difference for most of the traits. The value of PCV was higher than GCV for all the twenty-two characters. However, maximum GCV and PCV were observed for root dry weight plant-1 (31.44% and 32.17%) followed grain yield plant-1 (29.97% and 31.03%), root volume (28.62% and 29.20%), root fresh weight plant-1 (28.51% and 29.08%), biological yield plant-1 (21.86% 22.50%) and number of grains panicle-1 (20.55% and 21.37%). Rest of the traits showed moderate and low GCV and PCV. High heritability and genetic advance were recorded for the traits viz., leaf length, number of tillers plant-1, number of grains panicle-1, 1000 seed weight, root length, root volume, root fresh weight plant-1, root dry weight plant-1, kernel length-breadth ratio, grain yield plant-1, biological yield plant-1 and harvest index. The information regarding different variability will provide direction to select high yielding genotypes under aerobic condition.

Studies on Correlation and Path Coefficient Analysis in Hybrid Rice (Oryza sativa L.) for Yield and Quality Traits

The present investigation was carried out at Regional Agricultural Research Station, Polasa, Jagtial, Telangana state, India to study the correlation and path coefficient analysis towards yield, physical and chemical quality traits in 46 genotypes including two checks in Randomized Block Design with two replications during kharif, october, 2019. Association of yield and yield components and among grain yield characters makes us to understand their relationship towards selecting a high yielding and good quality varieties. The result from the study revealed that all the nineteen characters studied has shown a great range of variation for correlation and path analysis. The character association studies in this experiment revealed that the trait grain yield plant-1 had showed significant positive correlation with plant height, spikelet fertility, 1000 grain weight, milling %, hulling %, kernel length and kernel breadth whereas it showed negative and non-significant association with days to 50% flowering. The path analysis studies revealed that kernel length was the major contributor for grain yield plant-1 followed by plant height, spikelet fertility, number of grains per panicle, 1000 grain weight, milling %, gel consistency, amylose content and alkali spreading value. These characters showed direct positive effects for grain yield plant-1. From the study it can be concluded that the above characters can be used directly as the selection criteria in any rice yield improvement breeding programmes.

Classification of Grain s and Quality Analysis u sing Deep Learning

There are various varieties of Rice and lentils. Price fabrication and adulteration have been some of the various issues faced by the consumers, farmers and wholesale retailers. Traditional methods for Identification of these similar types of grains and their quality analysis are crude and inaccurate. Methods were tried to implemented earlier but due to financial inability and low efficiency, they weren’t successful. To overcome this problem, the project proposes a method that uses a machine learning technique for identification and quality analysis of these grains. Rice and Lentils which have the maximum consumption have been selected. Lentils are designated into classes based on colors. The technique of determining the elegance of a lentil is with the aid of seed coat shade. Red lentils can be confirmed through the cotyledon coloration. Lentil types may also have a huge variety of seed coat colors from inexperienced, red, speckled inexperienced, black and tan. The cotyledon colour may be red, yellow or inexperienced. The size and color of every Indian Lentil type (i.e. Red, Green, and Yellow, Black, White) are decided to be large or Medium or small, then size and colour end up part of the grade name. An smart machine is used to perceive the kind of Indian lentils from bulk samples. The proposed machine allows kernel length and coloration size using picture processing techniques. These Lentil size measurements, when combined with color attributes of the sample, classify three lentil varieties commonly grown in India with the highest accuracy. Rice is one of most consumed grains in India so its quality is of utmost importance. In this project, we identify and grade five types of rice and grade them with the help of their distinguished features such as size, color, shape, and surface. The project works in three phases viz., Feature Extraction, Training, and Testing. Various rice grain has a different shape, size, surface and various lentils come in different colors, Hence the feature that will be extracted is texture and colors. The method of regression will be adopted for the grading mechanism where the output will be in terms of percentage purity. The methodology for the extraction of the feature will be GLCM and Edge Detection where for supervised learning SVM and Back Propagation will be utilized. The project provides an efficient replacement for the traditional grading mechanism and standardizes the pricing of farm products based on their quality only.

Inheritance of some quantitative traits in the hybrid generations ‘Kuboyar × Gagat’

Rice is an important food for humans. In the world, for food purposes white-grain rice varieties are mainly used, but there are varieties in which kernel pericarp is of red, brown, purple or black color. Such rice is more beneficial for health promotion. The current paper has presented the results of genetic analysis of the inheritance of a number of variable quantitative traits in rice hybrid populations of the first and third generations obtained by the hybridization of the varieties ‘Kuboyar’ and ‘Gagat’. The variety ‘Kuboyar’ is medium-sized, with a compact erect panicle, an oval caryopsis and white pericarp. The variety ‘Gagat’ is tall, with a long drooping panicle, a long kernel and black pericarp. The work was carried out on the territory of the OP ‘Proletarskoe’ of the Rostov region in 2018–2020. The analysis of the inheritance of some quantitative traits that have a direct impact on the rice productivity made it possible to establish new inheritance regularities. According to the trait ‘plant height’ there has been identified partial dominance of the largest values of the trait in the hybrids F2 and F3. Allelic differences of 3 pairs of genes were identified in parental forms. The trait ‘panicle length’ showed overdominance of the trait in F2, it was absent in F3, and there were digenic differences in parental forms. The trait ‘number of spikelets per panicle’ demonstrated overdominance of large values and positive transgression. According to the trait ‘1000-grain weight’, the hybrids of both generations were split by the digenic scheme in a ratio of 1:4:6:4:1. There was no dominance in the trait ‘kernel length’; the initial varieties had differences in 2 pairs of genes. According to the trait ‘kernel width’, there was identified incomplete dominance of smaller values, and there was monohybrid split. There have been selected the best plants F3 with a black pericarp, average values of the traits ‘plant height’, ‘panicle length’, ‘1000-grain weight’ and an increased number of grains per panicle for the further breeding process.

Genetic identification and characterization of chromosomal regions for kernel length and width increase from tetraploid wheat

Background Improvement of wheat gercTriticum aestivum L.) yield could relieve global food shortages. Kernel size, as an important component of 1000-kernel weight (TKW), is always a significant consideration to improve yield for wheat breeders. Wheat related species possesses numerous elite genes that can be introduced into wheat breeding. It is thus vital to explore, identify, and introduce new genetic resources for kernel size from wheat wild relatives to increase wheat yield. Results In the present study, quantitative trait loci (QTL) for kernel length (KL) and width (KW) were detected in a recombinant inbred line (RIL) population derived from a cross between a wild emmer accession ‘LM001’ and a Sichuan endemic tetraploid wheat ‘Ailanmai’ using the Wheat 55 K single nucleotide polymorphism (SNP) array-based constructed linkage map and phenotype from six different environments. We identified eleven QTL for KL and KW including two major ones QKL.sicau-AM-3B and QKW.sicau-AM-4B, the positive alleles of which were from LM001 and Ailanmai, respectively. They explained 17.57 to 44.28% and 13.91 to 39.01% of the phenotypic variance, respectively. For these two major QTL, Kompetitive allele-specific PCR (KASP) markers were developed and used to successfully validate their effects in three F3 populations and two natural populations containing a panel of 272 Chinese wheat landraces and that of 300 Chinese wheat cultivars, respectively. QKL.sicau-AM-3B was located at 675.6–695.4 Mb on chromosome arm 3BL. QKW.sicau-AM-4B was located at 444.2–474.0 Mb on chromosome arm 4BL. Comparison with previous studies suggested that these two major QTL were likely new loci. Further analysis indicated that the positive alleles of QKL.sicau-AM-3B and QKW.sicau-AM-4B had a great additive effect increasing TKW by 6.01%. Correlation analysis between KL and other agronomic traits showed that KL was significantly correlated to spike length, length of uppermost internode, TKW, and flag leaf length. KW was also significantly correlated with TKW. Four genes, TRIDC3BG062390, TRIDC3BG062400, TRIDC4BG037810, and TRIDC4BG037830, associated with kernel development were predicted in physical intervals harboring these two major QTL on wild emmer and Chinese Spring reference genomes. Conclusions Two stable and major QTL for KL and KW across six environments were detected and verified in three biparental populations and two natural populations. Significant relationships between kernel size and yield-related traits were identified. KASP markers tightly linked the two major QTL could contribute greatly to subsequent fine mapping. These results suggested the application potential of wheat related species in wheat genetic improvement.

Genetic Dissection of Yield Determinants in Advance Breeding Lines (ABLs) of Rice (Oryza sativa L.) under Irrigated Condition of Chhattisgarh, India

The present study was undertaken to study the extent of genetic variability, character association and path analysis in 53 irrigated late duration breeding lines for 12 quantitative and 10 quality parameters. Analysis of variance revealed the presence of significant variation among the breeding lines for all the characters except for total tillers per plant, effective tillers per plant, spikelet fertility percentage and grain yield per plant. The highest magnitude of PCV and GCV were displayed by grain yield per plant followed by harvest index, filled spikelet per panicle and 1000 seed weight. The greater extent of heritability in broad sense were recorded for days to maturity, days to 50% flowering, 1000 seed weight, plant height, total spikelet per panicle, kernel length, kernel L:B ratio, grain length, head rice recovery percentage, amylose content, hulling and milling percentage. However, high heritability coupled with high genetic advance as percent of mean were displayed by plant height, number of spikelet per panicle, filled spikelet per panicle,1000 seed weight, kernel length, kernel L:B ratio, grain length, head rice recovery percentage and amylose content. Grain yield per plant exhibited positive significant correlation with harvest index, effective tillers per plant, 1000 seed weight, spikelet fertility %, days to maturity and days to 50% flowering. Path analysis revealed direct effect on grain yield were exerted by days to 50% flowering followed by harvest index, 1000 seed weight, total tillers per plant, number of spikelet per plant, effective tillers per plant and plant height indicating their importance in rice breeding programs.

Aromatic Rice of India: It’s Types and Breeding Strategies

The coalescence of organoleptic traits viz., pleasant aroma, cooked rice texture, and taste make aromatic rice unique and distinguished from non-aromatic rice. Aromatic rice is cultivated in every rice growing country; with each country has its own indigenous collection. International trade of rice is dominated by Indica (long grained), Japonica (short grained), aromatic rice (Basmati and Jasmine) and glutinous rice; amidst which, Basmati types from India and Pakistan; and Jasmine types from Thailand have phenomenal demand. In India all types of aromatic rice are cultivated based on Kernel length; short, medium, long and very long grained. Basmati varieties own the major market, while other types of aromatic rice besides Basmati are popular in local market only. The country inherits rich diversity of aromatic rice germplasm; with more than 300 different types, each of the rice growing states of India has its own locally popular aromatic rice varieties. India a country where two third of its population consume rice as part of their daily food; aromatic rice always remain their favorite. Basmati, by virtue of its excellent qualities it dominates both national and international market. Every year, Basmati ranks first in respect of foreign exchange earned from the export of agricultural products from India (APEDA). The phenomenal demand and export figures have augmented Basmati Breeding program. However, only few aromatic varieties are cultivated depending on their demand, and their breeding program is also limited. In India, Basmati has over-shadowed other types of aromatic rice in market and in plant breeding programs too. Breeding for Basmati varieties is undertaken by prime agricultural institutions of India. The country regulates quality standards and development of Basmati varieties with the help of Export of Basmati Rice (Quality Control and Inspection) Rules 2003; Agricultural and Processed Food Products Export Development Authority (APEDA); and Basmati Export Development Foundation (BEDF). However, no such initiatives have been taken to promote the development of other aromatic rice varieties of India besides Basmati.

Genetic dissection of grain architecture-related traits in a winter wheat population

Background The future productivity of wheat (T. aestivum L.) as the most grown crop worldwide is of utmost importance for global food security. Thousand kernel weight (TKW) in wheat is closely associated with grain architecture-related traits, e.g. kernel length (KL), kernel width (KW), kernel area (KA), kernel diameter ratio (KDR), and factor form density (FFD). Discovering the genetic architecture of natural variation in these traits, identifying QTL and candidate genes are the main aims of this study. Therefore, grain architecture-related traits in 261 worldwide winter accessions over three field-year experiments were evaluated. Results Genome-wide association analysis using 90K SNP array in FarmCPU model revealed several interesting genomic regions including 17 significant SNPs passing false discovery rate threshold and strongly associated with the studied traits. Four of associated SNPs were physically located inside candidate genes within LD interval e.g. BobWhite_c5872_589 (602,710,399 bp) found to be inside TraesCS6A01G383800 (602,699,767–602,711,726 bp). Further analysis reveals the four novel candidate genes potentially involved in more than one grain architecture-related traits with a pleiotropic effects e.g. TraesCS6A01G383800 gene on 6A encoding oxidoreductase activity was associated with TKW and KA. The allelic variation at the associated SNPs showed significant differences betweeen the accessions carying the wild and mutated alleles e.g. accessions carying C allele of BobWhite_c5872_589, TraesCS6A01G383800 had significantly higher TKW than the accessions carying T allele. Interestingly, these genes were highly expressed in the grain-tissues, demonstrating their pivotal role in controlling the grain architecture. Conclusions These results are valuable for identifying regions associated with kernel weight and dimensions and potentially help breeders in improving kernel weight and architecture-related traits in order to increase wheat yield potential and end-use quality.