Symbiotically essential genes have been identified in Rhizobium melilodi that are structurally and functionally related to chromosomal virulence (chv) genes of Agrobacterium tumefaciens. Homologous sequences also exist in the genomes of other fast-growing rhizobia including Rhizobium trifolii, Rhizobium leguminosarum, and Rhizobium phaseoli. In Agrobacterium, the chvA and chvB loci are known to be essential for oncogenic transformation of The family Rhizobiaceae has classically been considered to contain only two genera, Agrobacterium and Rhizobium. Agrobacterium species are plant pathogens that induce tumorous growths on a wide variety of dicotyledonous plants, while Rhizobium species are agriculturally beneficial plant symbionts that induce nitrogen-fixing nodules on the roots of legumes. We report here what is to our knowledge the first instance in which a group of symbiotically required Rhizobium genes has been shown to be structurally and functionally related to the Agrobacterium genes that are required for pathogenesis.Most of the genes necessary for virulence (vir) of Agrobacterium tumefaciens have been localized to a unique endogenous plasmid called the Ti plasmid (1). Mutations in these vir genes prevent oncogenic transformation, presumably by interfering with the successful transfer to the plant of another region on the Ti plasmid called T-DNA, that encodes enzymes involved in phytohormone production (2)(3)(4). In addition to plasmid-encoded vir genes, two closely linked virulence loci have been found in the chromosome of Agrobacterium (5). These loci, designated chvA and chvB, have the interesting feature that mutations at either locus interfere with the ability of Agrobacterium to bind to plant cells. Little is known about how the chv gene products function, but chv mutants show pleiotropic effects likely to be related to cell envelope changes (6, 7).The data presented here show that chvA and chvB are homologous to DNA sequences in the genomes of four different fast-growing Rhizobium species and that in the case ofRhizobium meliloti, the corresponding genes can functionally complement Agrobacterium chv mutants. R. meliloti mutants in the chv-equivalent loci are still able to induce nodule-like structures on alfalfa, but such nodules do not show normal bacterial invasion and differentiation. MATERIALS AND METHODSStrains and Plasmids. The following Rhizobium strains were used in this study: R. meliloti 102F34 (8), 1021 (9) and 41 (10); Rhizobium phaseoli 8002 (11) and its sym plasmidcured derivative 8400 (11); Rhizobium leguminosarum 128C53 (12) and its sym plasmid-cured derivative B151 (12); Rhizobium trifolii 162X68, from Nitragin (Milwaukee, WI), and RS 800 (13); Rhizobium japonicum USDA 110 (14). Agrobacterium strains have previously been described: A348 is A. tumefaciens C58 chromosome carrying pTiA6NC (15); Tn5 and Tn3HoHol insertion mutants were used for complementation studies (5). Escherichia coli strains were HB101 (pro, leu, thi, lacY, endoI, recA, hsdR, hsdM, str?) and HB1O1::Tn5....

The high performance and stability of wheat genotypes for yield, grain protein content (GPC), and other desirable traits are critical for varietal development and food and nutritional security. Likewise, the genotype by environment (G × E) interaction (GEI) should be thoroughly investigated and favorably utilized whenever genotype selection decisions are made. The present study was planned with the following two major objectives: 1) determination of GEI for some advanced wheat genotypes across four locations (Ludhiana, Ballowal, Patiala, and Bathinda) of Punjab, India; and 2) selection of the best genotypes with high GPC and yield in various environments. Different univariate [Eberhart and Ruessll’s models; Perkins and Jinks’ models; Wrike’s Ecovalence; and Francis and Kannenberg’s models], multivariate (AMMI and GGE biplot), and correlation analyses were used to interpret the data from the multi-environmental trial (MET). Consequently, both the univariate and multivariate analyses provided almost similar results regarding the top-performing and stable genotypes. The analysis of variance revealed that variation due to environment, genotype, and GEI was highly significant at the 0.01 and 0.001 levels of significance for all studied traits. The days to flowering, plant height, spikelets per spike, grain per spike, days to maturity, and 1000-grain weight were specifically affected by the environment, whereas yield was mainly affected by the environment and GEI. Genotypes, on the other hand, had a greater impact on the GPC than environmental conditions. As a result, a multi-environmental investigation was necessary to identify the GEI for wheat genotype selection because the GEI was very significant for all of the evaluated traits. Yield, 1000-grain weight, spikelet per spike, and days to maturity were observed to have positive correlations, implying the feasibility of their simultaneous selection for yield enhancement. However, GPC was observed to have a negative correlation with yield. Patiala was found to be the most discriminating environment for both yield and GPC and also the most effective representative environment for GPC, whereas Ludhiana was found to be the most effective representative environment for yield. Eventually, two NILs (BWL7508, and BWL7511) were selected as the top across all environments for both yield and GPC.

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I1 plots had higher germination (5.61%), millable canes (9.09%), brix (5.19%), yield (0.6%) and CCS (t/ha) (2.35 and 4.37% at 10th and 12th months), respectively. K3 plots reported significantly higher performance as compared to K1 and K2 treatments while being at par with the K4 treatment. Benefits were reported to be highest at K3 treatment under water stressed conditions. Hence, 80 kg K2O/ha under water stressed and potash deficient soils prove to be a better option for better cane growth, yield and quality parameters.