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Predicting the influence of fertilization regimes on potential N fixation through their effect on free-living diazotrophic community structure in double rice cropping systems

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TLDR
In this article, the long-term effects of partial substitution of mineral N by organic fertilizers on diazotroph abundance and composition in double rice cropping systems were investigated through high-throughput sequencing, network analysis, and 15N2 labelling methods.
Abstract
Free-living nitrogen fixation (FLNF) by diazotrophs is a ubiquitous renewable resource and may constitute an attractive viable solution to the problem of environmental degradation caused by nitrogen over-fertilization. However, the biotic and abiotic control mechanisms of free-living nitrogen fixation are poorly understood. Moreover, there is rare information to link the composition and functions of diazotrophic communities; additionally, there are insufficient or inadequate predictors of the potential N fixation rate (PNFR) to help improve fertilization strategies. Here, through high-throughput sequencing, network analysis, and 15N2 labelling methods, we investigated the long-term effects of partial substitution of mineral N by organic fertilizers on diazotroph abundance and composition in double rice cropping systems. The field trial was conducted for 34 years and included five treatments: control without fertilizer (CK); only mineral NPK fertilizer (NPK); 70% inorganic N + 30% organic N + PK (NPKM1); 50% inorganic N + 50% organic N + PK (NPKM2); 30% inorganic N + 70% organic N + PK (NPKM3). Our results revealed that at the same nutrient input level, increasing the percentage of organic N substitution reduced diazotrophic abundance. Compared with the CK and NPK treatments, the organic substitution regimes reduced the fluctuation range of the PNFR between the early and late rice growing seasons. NH4+-N was the primary factor that negatively correlated (p

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Soil-plant-microbiota interactions to enhance plant growth

TL;DR: In this article , a review outlines the characteristics of soil-plant-microbiota interactions that would lead to enhanced plant growth and the importance of characterizing the soil microbial communities with metabarcoding and shotgun metagenomics allied to machine learning analytics.
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Alpine meadow degradation depresses soil nitrogen fixation by regulating plant functional groups and diazotrophic community composition

TL;DR: In this paper , the authors investigated changes in soil BNF and their potential drivers in alpine meadows along a degradation gradient on the Tibetan Plateau (non-degraded, lightly degraded, moderately degraded, and severely degraded meadows) using real-time quantitative PCR and amplicon sequencing.
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Nutrient status of integrated rice-crayfish system impacts the microbial nitrogen-transformation processes in paddy fields and rice yields.

TL;DR: In this article , the composition and functional characteristics of soil microbial communities from six rice-crayfish (IRC) farms with variant nutrient statuses in the Yangtze River Delta were surveyed.
Journal ArticleDOI

Temporal dynamics of free‐living nitrogen fixation in the switchgrass rhizosphere

TL;DR: It is found that climate variables are strong drivers of FLNF rates in switchgrass systems, compared to other environmental and biological factors including soil nutrients and diazotrophic community composition, and seasonal FLNF N contributions, based on measurement with high temporal resolution, has the potential to meet up to 80% of switchgrass N demands.
Journal ArticleDOI

Variations of Bacterial and Diazotrophic Community Assemblies throughout the Soil Profile in Distinct Paddy Soil Types and Their Contributions to Soil Functionality

TL;DR: In this article , the vertical distribution of bacterial and diazotrophic communities in two hydragric anthrosols was analyzed. And the results revealed that nitrogen and iron status were the most distinguishing edaphic characteristics of the two soil types throughout the soil profile.
References
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Journal Article

R: A language and environment for statistical computing.

R Core Team
- 01 Jan 2014 - 
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
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Random Forests

TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.

Classification and Regression by randomForest

TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.
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