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RISS 인기검색어
- 검색키워드
- 저자 : ( Jose R Valverde ) (검색결과 2 건)
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Effect of Herbicide Combinations on Bt-Maize Rhizobacterial Diversity
( Jose R Valverde ),( Silvia Marin ),( Rafael P Mellado ) 한국미생물 · 생명공학회 2014 Journal of microbiology and biotechnology Vol.24 No.11
Reports of herbicide resistance events are proliferating worldwide, leading to new cultivation strategies using combinations of pre-emergence and post-emergence herbicides. We analyzed the impact during a one-year cultivation cycle of several herbicide combinations on the rhizobacterial community of glyphosate-tolerant Bt-maize and compared them to those of the untreated or glyphosate-treated soils. Samples were analyzed using pyrosequencing of the V6 hypervariable region of the 16S rRNA gene. The sequences obtained were subjected to taxonomic, taxonomy-independent, and phylogeny-based diversity studies, followed by a statistical analysis using principal components analysis and hierarchical clustering with jackknife statistical validation. The resilience of the microbial communities was analyzed by comparing their relative composition at the end of the cultivation cycle. The bacterial communites from soil subjected to a combined treatment with mesotrione plus s-metolachlor followed by glyphosate were not statistically different from those treated with glyphosate or the untreated ones. The use of acetochlor plus terbuthylazine followed by glyphosate, and the use of aclonifen plus isoxaflutole followed by mesotrione clearly affected the resilience of their corresponding bacterial communities. The treatment with pethoxamid followed by glyphosate resulted in an intermediate effect. The use of glyphosate alone seems to be the less aggressive one for bacterial communities. Should a combined treatment be needed, the combination of mesotrione and s-metolachlor shows the next best final resilience. Our results show the relevance of comparative rhizobacterial community studies when novel combined herbicide treatments are deemed necessary to control weed growth.
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Jose Luis Zepeda-Batista,Luis Antonio Saavedra-Jimenez,Agustin Ruiz-Flores,Rafael Nunez-Dominguez,Rodolfo Ramirez-Valverde 아세아·태평양축산학회 2017 Animal Bioscience Vol.30 No.12
Objective: From a review of published information on genetic association studies, a meta-analysis was conducted to determine the influence of the genes κ-casein (CSN3) and β-lactoglobulin (LGB) on milk yield traits in Holstein, Jersey, Brown Swiss, and Fleckvieh. Methods: The GLIMMIX procedure was used to analyze milk production and percentage of protein and fat in milk. Models included the main effects and all their possible two-way interactions; not estimable effects and non-significant (p>0.05) two-way interactions were dropped from the models. The three traits analyzed used Poisson distribution and a log link function and were determined with the Interactive Data Analysis of SAS software. Least square means and multiple mean comparisons were obtained and performed for significant main effects and their interactions (p<0.0255). Results: Interaction of breed by gene showed that Holstein and Fleckvieh were the breeds on which CSN3 (6.01%±0.19% and 5.98%±0.22%), and LGB (6.02%±0.19% and 5.70%±0.22%) have the greatest influence. Interaction of breed by genotype nested in the analyzed gene indicated that Holstein and Jersey showed greater influence of the CSN3 AA genotype, 6.04%±0.22% and 5.59%±0.31% than the other genotypes, while LGB AA genotype had the largest influence on the traits analyzed, 6.05%±0.20% and 5.60%±0.19%, respectively. Furthermore, interaction of type of statistical model by genotype nested in the analyzed gene indicated that CSN3 and LGB genes had similar behavior, maintaining a difference of more than 7% across analyzed genotypes. These results could indicate that both Holstein and Jersey have had lower substitution allele effect in selection programs that include CSN3 and LGB genes than Brown Swiss and Fleckvieh. Conclusion: Breed determined which genotypes had the greatest association with analyzed traits. The mixed model based in Bayesian or Ridge Regression was the best alternative to analyze CSN3 and LGB gene effects on milk yield and protein and fat percentages.
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