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No full textNot AvailableIn wheat, genomic prediction accuracy (GPA) was assessed for three micronutrient traits (grain iron, grain zinc, and β-carotenoid concentrations) using eight Bayesian regression models. For this purpose, data on 246 accessions, each genotyped with 17,937 DArT markers, were utilized. The phenotypic data on traits were available for 2013-2014 from Powerkheda (Madhya Pradesh) and for 2014-2015 from Meerut (Uttar Pradesh), India. The accuracy of the models was measured in terms of reliability, which was computed following a repeated cross-validation approach. The predictions were obtained independently for each of the two environments after adjusting for the local effects and across environments after adjusting for the environmental effects. The Bayes ridge regression (BayesRR) model outperformed the other seven models, whereas BayesLASSO (BayesL) was the least efficient. The GPA increased with an increase in the size of the training set as well as with an increase in marker density. The GPA values differed for the three traits and were higher for the best linear unbiased estimate (BLUE) (obtained after adjusting for the environmental effects) relative to those for the two environments. The GPA also remained unaffected after accounting for the population structure. The results of the present study suggest that only the best model should be used for the estimations of genomic estimated breeding values (GEBVs) before their use for genomic selection to improve the grain micronutrient contents.Not Availabl
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No full textNot AvailableSoil inorganic carbon (SIC) is important carbon reservoirs in desert ecosystems. However, little attention was paid to estimate carbon stock in these regions. In the present study, the distribution of SIC stock was investigated using digital soil mapping in Bikaner district, Rajasthan, India. A total of 187 soil profiles were used for SIC estimation by Quantile regression forest model. Landsat data, terrain attributes and bioclimatic variables were used as environmental variables. Ten-fold cross-validation was used to evaluate model. Equal-area quadratic splines were fitted to soil profile datasets to estimate SIC at six standard soil depths (0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm). The SIC in the study area ranged from 0.27 to 27.85 g kg−1 in 0–5 cm and 0.31 to 27.84 g kg−1 in 5–15 cm, respectively. The model could capture reasonable variability (R2 = 11–21%) while predicting SIC for different depths. The Lin’s concordance correlation coefficient values ranged from 0.20 to 0.32, indicating poor relationship between the predicted and observed values. The values of prediction interval coverage probability (PICP) recorded 86.4–91.1% for SIC at different depths. Annual precipitation and precipitation seasonality were the most important covariates in soil below the 30 cm depth. The predicted SIC stocks were 10.3 ± 0.01, 81.6 ± 0.07 and 186.7 ± 0.13 Mg ha−1 at 0–15, 0–100 and 0–200 cm, depth, respectively. The uncertainty analysis suggests that there is room to improve the current spatial predictions of SIC. It is anticipated that this digital mapping of SIC will be useful for assessment of carbon cycle in arid regions.Not Availabl
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No full textNot AvailableCauliflower is an important vegetable crop grown worldwide. Development and characterization
of suitable cytoplasmic male sterile (CMS) lines and male fertile inbreds is instrumental
in developing heterotic hybrids. A study was undertaken to estimate heterosis and combining
ability in snowball cauliflower lines through line × tester analysis involving five Ogura CMS
lines and seven male fertile testers. The assessment of F1s along with their parental lines
for different horticultural traits revealed a wide range of heterosis. Ogu-13-01 was the best
general combiner for leaf length, leaf width and plant height, Ogu-13-85 for number of
leaves/plant, Ogu-101 for days to 50% curd initiation, days to 50% curd maturity and net
curd weight and Ogu-119 for harvest index. The lines with better general combining ability
(GCA) were involved in majority of the heterotic hybrids. The tester Kt-18 was the best general
combiner for leaf width, DB-1305 for number of leaves/plant and DB-187 for leaf length,
plant height, gross plant weight and days to 50% curd initiation. However, GCA alone was not
sufficient to determine and identify the potential parental lines. Hybrids Ogu-101 × DB-1305
and Ogu-119 × Suprimax Late were the best specific combinations for marketable curd weight,
marketable curd yield, net curd weight and net curd yield. The hybrid Ogu-119 × Suprimax
Late was the best heterotic combination for marketable curd weight, marketable curd yield,
net curd weight and net curd yield, followed by Ogu-101 × DB-1305, Ogu-119 × Sel-26 and
Ogu-101 × DB-187. Our findings will be instrumental to identify suitable parental lines and
developing heterotic F1 hybrids in cauliflower.Not Availabl
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No full textNot AvailableThe effects of salinity adaptation on gut microbiota of juvenile Indian white shrimp, Penaeus
indicus H. Milne Edwards, 1837 was analysed in this experiment. Intestines of shrimps
adapted to two different salinities were examined using 16S rRNA amplicon sequencing.
At all salinity levels, the dominant phyla recorded were Proteobacteria followed by
Bacteroidota and Fusobacteriota. However, as the salinity decreased, abundance of
Proteobacteria increased while richness of Bacteroidota and Fusobacteriota decreased
signifi cantly. At genus level, salinity reduction increased the abundance of Vibrio and
Photobacterium; while it decreased the abundance of Spongiimonas and Hypnocyclicus.
The survival of animals in both the groups was not affected but the weight gain was less
in low saline group (5 ppt). The results confi rm the infl uence of salinity in rearing water
on the intestinal microbiota of shrimpNot Availabl
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No full textNot AvailableBlack pepper is highly sensitive to water deficit stress especially during summer, resulting in significant losses in yield; therefore, strategies aimed at enhancing water stress tolerance are essential. Melatonin improves stress tolerance in plants; however, its method of application and optimum concentration in black pepper under water deficit stress remains unclear. Therefore, we conducted a two pot culture experiment during March and April, 2022 (var. Panniyur-1) to investigate the effects of foliar-sprayed and root-irrigated melatonin (50, 100 and 150 μM L-1) on the recovery per cent and physiological mechanism under water stress. The treatment details were, WW - Well-watered; WS - Water stressed; FM50 - Waterstress+50μM Melatonin (Foliar spray); FRM50 - Water stress + 50μM Melatonin (Foliar spray + Soil drenching @ 50ml/plant); FM100 - Water stress + 100μM Melatonin (Foliar spray); FRM100 - Water stress + 100μM Melatonin (Foliar spray + Soil drenching @ 50ml/plant); FM150 - Water stress + 150μM Melatonin (Foliar spray); FRM150 - Water stress + 150μM Melatonin (Foliar spray + Soil drenching @ 50ml/plant). The melatonin-induced enhanced stress tolerance could be attributed to improved recovery %, leaf relative water content, photosynthetic pigments, activity of antioxidant enzymes (SOD, POD, and CAT), and ultimately significantly relieved the inhibitory effects of water stress on leaves. After rehydration, melatonin-treated plants recovered more quickly than untreated plants. In addition, melatonin counteracted the water stress induced accumulation in proline content. Overall, the results of this study demonstrated that melatonin at 100 μM L-1 (Foliar spray and root irrigation) significantly alleviated the adverse effects of water deficit stress compared untreated plants. In addition, application of exogenous melatonin combined with root and foliar application is superior than foliar spraying alone.Not Availabl
