The genebank at ICRISAT, India conserves 134 accessions of Deenanath grass (Pennisetum pedicellatum Trin.) from eight countries. A predicted probability map developed using FloraMap indicated 194 provinces in 21 countries of Asia and Africa as geographical gaps. All aceessions were annual and days to 50% flowering ranged from 43 to 109 days, number of total tillers/plant from 275 to 2,247 and number of productive tillers per plant from 114 to 1261. Accession IP 21821 produced maximum total tillers (2,247) and IP 21850 scored maximum (9) for forage yield potential. Early flowering (43 days) and high tillering (2,247) accessions of cluster 1 were considered as a promising source for early cuttings of green fodder. Mean diversity (H`) for quantitative traits (H`=0.591 + 0.010) was higher than that for qualitative traits (H`=0.284 + 0.089). Early flowering, high tillering habit and higher levels of resistance to downy mildew make the Deenanath grass important.

Black Gram (Vigna mungo L. Hepper) is most prevalent types out of pluses crops 80 genotypes of Black gram collected from Bastar Region (Dantewad, Sukma, Bijapur, Kondagoan, Narayanpur, Kanker districts). The experiment was conducted in Randomized Complete Block Design (RCBD) with two replications comprising two check varieties viz. Indira Urd Pratham and T.U. 94-2.Crop growing spacing 30×10cm during Kharif 2019 in Research cum Instructional Farm of SGCARS Kumhrawand, Jagdalpur, Bastar (C.G.). Analysis of variances indicated presences of significant genetic variability for thirteen biometrical traits in Black Gram genotypes. Highest GCV and PCV estimated for seed yield per plant, plant height (cm), number of primary branches per plant and test weight of thousand seeds. High heritability and genetic advances indicated least influenced of environment on the expression of character and characters governed by additive genes respectively. High heritability accompanied with high Genetic Advance as percent of mean observed for the traits plant height (cm), Test weight of thousand seeds, seed yield per plant (g), harvest index (%) and petiole length (cm) due to additive gene effect abundant sources provided for improving the traits though section.

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Aim: To identify the best sequence of pre- and post-emergence herbicides for achieving better weed control efficiency in aerobic rice. Methodology: A field experiment was conducted in Randomized Block Design with eleven treatment combinations, replicated thrice.? The dominant weeds in field were C. dactylon, E. colona and E. crusgalli among grasses, C. rotundus, C. difformis and F. maliaceae among sedges and C. axillaris and P. niruri among broad-leaf weeds. Treatments consisting sequential application of two pre-emergence application [Pendimethalin (30 EC) @ 1.00 kg a.i. ha-1; Butachlor (50 EC) @ 1.5 kg a.i. ha-1] followed by three post emergence herbicides [Bispyribac-Na (10% SC) @ 35 g a.i. ha-1; 2, 4-D Na salt (80 WP) @ 0.06 kg a.i. ha-1; Almix (CME + MSM ) (20 WP) @ 40 g a.i. ha-1] and straw mulching @ 4 t ha-1;? Mechanical weeding at 20 and 45 DAS, weed free and unweeded check.? Results: Among herbicidal treatments, pre-emergence application of pendimethalin at 3-4 DAS fb Bispyribac-Na at 15-20 DAS as post-emergence was most effective in minimizing weed density (4.81 m-2), biomass (6.20 g m-2), weed index (1.11%) and in enhancing the weed control efficiency (84.50%), grain yield (3.68 t ha-1) and straw yield (4.87 t ha-1) over rest of the treatments. Interpretation: Sequential application of pendimethalin at 3-4 DAS fb bispyribac-Na at 15-20 DAS is prominent in enhancing herbicide efficacy and reducing weed flora abundance resulting in higher weed control efficiency and grain yield due to their broad spectrum weed control.

Machine learning is the ability of machines to learn from past experiences using historical data (supervised and semisupervised cases) to solve a given problem. Machines make decisions using Artificial Intelligence (past experiences). Companies adequately understand that increasing business efficiency and employee productivity are of supreme importance to thriving in a highly competitive digital environment. Any process can be automated as long as there is a clear operating procedure available. In today’s time, almost every sector is automating things to minimise cost and improve reliability. So, we need something that will resolve this. In this paper, we will study the many good and bad impacts of artificial intelligence, robotics, and automation on employment. We will also discuss some ideas to minimise the bad impact and maximise the good impact in the related fields