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An experiment was conducted to investigate the influence of packaging materials and seed treatments on storability of cluster bean under ambient conditions of Bengaluru. The experiment consisted of six treatments viz. control (T 1 ), bavistin @ 2 g kg -1 (T 2 ), spinosad @ 0.04 ml/kg (T 3 ), neem leaf powder @ 1:20 ratio (T 4 ), Acorus calamus@ 10 g kg -1 (T 5 ) and cow dung powder @ 10 g kg -1 (T 6 ) and three packaging materials viz., cloth bag (C 1 ) super grain bag (C 2 ) and poly lined cloth bag (C 3 ). Treated seed samples were stored in three containers under ambient storage conditions up to the duration of which seeds maintain minimum seed certification standards and samples were drawn at bimonthly intervals for ascertaining the seed quality parameters. The study suggested that seed treatment could be useful to prolong the storage life of cluster bean seeds. The seeds treated with spinosad (0.04 ml/kg) and stored in super-grain bag were better for maintenance of higher seed quality parameters [germination (80.00%), root length (11.70 cm), shoot length (13.60 cm), mean seedling dry weight (152 mg), seedling vigour index I and II (2024&12140) and TDH activity (1.224) with low electrical conductivity (0.368 dSm )] up to 18 months under ambient conditions of Bengaluru (room temperature). Super-grain bag proved to be better storage container with higher seed quality attributes viz., germination (72.38 %), seedling vigour index-I (1726), total dehydrogenase activity (1.201) and other seed quality parameters compared to cloth bag. The study suggested that use of appropriate packaging material and seed treatment could be useful to prolong the storage life of cluster bean seeds.

An experiment was carried to find out the effect of sowing dates and cutting time on seed yield and quality of alfalfa cv. RL-88. The results of the experiment revealed that, the significantly higher seed yield per plant (1.491 g), seed yield per plot (161.07 g) and seed yield (355.00 kg ha -1 ) was noticed in 15 th August sowing and the cutting carried out at 60 days after sowing. Whereas, the seed quality parameters of the resultant seeds viz., highest seed germination (95.00 %), seedling length (13.90 cm), seedling dry weight (412.69 mg) and seedling vigour index (1320) found highest in15 th August sowing and the cutting followed at 60 days after sowing.

The present study aimed at assessing the extent and pattern of genetic diversity within a core set of soybean germplasm comprising of 98 accessions. A total of thirty-one morphometric traits were studied, among them qualitative traits viz., leaf shape, flower color, seed coat color, and hypocotyl colour showed a higher genetic diversity with higher diversity indices. The variability parameters like mean, range of variation, GCV, PCV, heritability and genetic advance were estimated for 18 quantitative traits. The differences between GCV and PCV estimates were narrow for most quantitative traits indicating less contribution of environmental factors in traits expression. High estimates of heritability coupled with high genetic advance were observed in all quantitative traits except for days to maturity. The traits with higher heritability and GA value may indicate their variability and high selective value. Expression of lines in biplots using the first four principal components explains 79.10% of total variation and says black and yellow seeded genotypes have higher and lower variability to exploit, respectively. Hence, selection pressure could profitably be applied to these traits for their improvement.

Background: Identification of suitable factors that influence significantly to the response is crucial for the traits based breeding program to make a better decision about improvement in productivity. Multiple linear regression (MLR) is the benchmark method commonly using to identify suitable factors for crop improvement. It doesn’t work always due to stringent assumption (Multicollinearity, Linearity) behind the MLR model. Here we tried to develop an efficient model for the selection of major traits that contribute to seed yield in soybean by comparing different models.Methods: Field experiment was conducted using 98 soybean core population through augmented design.18 morphometric traits obtain from soybean core population were considered under the study as regressors.Multiple linear regression (MLR), Principle component Regression (PCR), Regression tree and Random Forest models were compared to select traits based on prediction accuracy.Result: All the models identified the number of pods per plant (NPP) has the most influencing variable to the soybean yield. However random forest has a much higher prediction power (RMSE=4.59, MAPE=0.18) compared to other models under study. The results of random forest revealed that the number of pods per plant, number of branches per plant and other associated characters like plant height at harvest as highly influencing traits for seed yield in soybean.Finally, tried to identify genotypesthat possess superiority about most influencing morphological characters on seed yield using cluster analysis.