首页> 外文期刊>Canadian journal of microbiology >Predicting survival of a genetically engineered microorganism, Pseudomonas chlororaphis 3732RN-L11, in soil and wheat rhizosphere across Canada with linear multiple regression models.
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Predicting survival of a genetically engineered microorganism, Pseudomonas chlororaphis 3732RN-L11, in soil and wheat rhizosphere across Canada with linear multiple regression models.

机译:使用线性多元回归模型预测基因工程微生物,即假单胞菌(Pseudomonas chlororaphis)3732RN-L11在加拿大土壤和小麦根际中的存活。

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Pseudomonas chlororaphis 3732RN-L11 survival rates in soil and wheat rhizosphere were measured using intact soil core microcosms representing 23 sites across Canada. Linear multiple regression (LMR) models were developed to predict the survival rate of this genetically engineered microorganism (GEM) as a function of soil parameters measured at the time of microcosm inoculation. LMR models were tested by comparing their predicted survival rates with observed survival rates from environmental introductions of the GEM by Gagliardi et al. (2001) at five field sites across Canada over two years. No soil parameter (e.g., % clay) was highly correlated with GEM survival rates in soil or wheat rhizosphere. Total fungal colony-forming units (CFUs), % soil titanium (positive correlations), and % soil magnesium (negative correlation) were found to be the best LMR predictors of GEM survival rates in soil over two years. Total soil bacterial CFUs, nitrate, % soil potassium (positive correlations), and exchangeable magnesium (negative correlation) were found to be the best LMR predictors of GEM survival rate in wheat rhizosphere over two years. While LMR models were statistically significant, they were unable to reliably predict the survival rate of the GEM in field trial introductions. The results indicate that there can be considerable uncertainty associated with predicting GEM survival for multi-site environmental introductions.
机译:使用代表了加拿大23个站点的完整土壤核心微观世界,测量了绿脓杆菌3732RN-L11在土壤和小麦根际中的存活率。开发了线性多元回归(LMR)模型来预测该基因工程微生物(GEM)的存活率,作为微生物接种时测得的土壤参数的函数。通过比较LMR模型的预测存活率和Gagliardi等人从环境条件下引入GEM观察到的存活率来测试LMR模型。 (2001)在加拿大的五个现场工作了两年。在土壤或小麦根际中,没有任何土壤参数(例如粘土含量)与GEM存活率高度相关。发现总真菌菌落形成单位(CFU),土壤钛百分比(正相关)和土壤镁百分比(负相关)是两年内土壤中GEM存活率的最佳LMR预测指标。发现总土壤细菌CFU,硝酸盐,%土壤钾(正相关)和可交换镁(负相关)是两年内小麦根际GEM存活率的最佳LMR预测指标。尽管LMR模型具有统计学意义,但它们无法可靠地预测野外试验中GEM的存活率。结果表明,在多地点环境引入中,与预测GEM存活相关的不确定性很大。

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