Introduction of crop rotation and rice straw application in a former flooded rice system and their impact on the microbial community in bulk soil and the rhizosphere of <em>Zea mays</em>

Rice is one of the most important staple foods The constant flooding of rice fields results in anoxic conditions in soil, providing an optimal habitat for methane producing Archaea. The produced methane is the main substrate for methanotrophic bacteria that oxidize it to carbon dioxide (CO2) and therefore represent a methane sink within rice fields. To decrease water consumption and minimize methane emissions, an altered crop rotation by introducing plants such as maize cultivated under upland conditions, is considered. However previous studies observed, that such crop rotation promotes the development of desiccation cracks and due to that a leaching of carbon and nitrogen, whereby an increase emission of nitrous oxide was also observed. To prevent these losses, rice straw can be applied as mulch to conserve the soil moisture content and stabilize soil aggregates. How the microbial community response on the introduction of crop rotation and straw application is a central question in this thesis. Furthermore, the analysis of microbial community could indicate whether a combination of these two agricultural management practises solve the problem of high methane emissions. We analysed the microbial communities in rice fields in comparison to rice-maize crop rotation soils. The microbial community of paddy soil is well studied and characterized by an appearance of methanogens and methanotrophs. We additionally focussed on the rice phyllosphere and could isolated methanotrophs that are potentially able to consume methane emitted by rice plants. To have a closer look at the impact of crop rotation on bacteria, archaea and fungi, we analysed the microbial communities during maize cultivation in rice monoculture soils, rice-maize crop rotation soils and, as a control, in a maize monoculture soils, via amplicon sequencing. This revealed that microbial communities in crop rotation soils have a higher similarity to those in rice soils than in maize soil. However, differences between the communities in rice soils compared to crop rotation soils were mainly due to a depletion of anaerobic microbes in crop rotation soils. We investigated the active straw degrading bacterial and fungal community in detail in a crop rotation soil by applying highly labelled 13C-rice straw to the bulk soil and rhizosphere of maize. The results showed that straw degradation was performed by aerobic microorganisms in crop rotation soil, which underwent a clear temporal succession. In the rhizosphere we detected partly different microorganisms as labelled than in bulk soil, indicating that host plant specific taxa benefit from straw in the rhizosphere. Nevertheless, the lower label intensity in the rhizosphere indicates that rhizosphere organisms use straw as additional carbon source with lower efficiency besides the rhizodeposits. To investigate specifically the root exudate consuming microorganisms in the rhizosphere, we conducted a labelling experiment of maize with 13CO2 and subsequent phospholipid fatty acid stable isotope probing. The results confirm that the addition of straw impacts the uptake of root exudates. Obviously, a simultaneous use of root exudates and straw takes place, because straw addition results in a decreased uptake of root exudates. This thesis provides evidence that the introduction of crop rotation leads to an altered microbial community in bulk soil and in the rhizosphere of maize. The depletion of methanogens leads to the assumption that a crop rotation of rice, followed by maize, and straw addition during the dry season is a promising strategy to reduce methane emissions

Data_Sheet_1_Crop Rotation and Straw Application Impact Microbial Communities in Italian and Philippine Soils and the Rhizosphere of Zea mays.pdf

<p>Rice is one of the most important nourishments and its cultivation binds large agricultural areas in the world. Its cultivation leads to huge water consumption and high methane emissions. To diminish these problems, crop rotation between paddy rice and maize is introduced in Asia, but can lead to losses of carbon and water by the formation of desiccation cracks. To counteract these problems rice straw can be applied. We analyzed soil microbial responses to different crop rotation systems [rice–rice (RR), maize–maize (MM), maize–rice (MR)] and to rice straw application in the soil and rhizosphere of maize. Zea mays was grown in microcosms using soils from different field locations, each including different crop rotation regimes. The bacterial and fungal community composition was analyzed by 16S rRNA gene and ITS based amplicon sequencing in the bulk soil and rhizosphere. The microbiota was clearly different in soils from the different field locations (analysis of similarity, ANOSIM: R = 0.516 for the bacterial community; R = 0.817 for the fungal community). Within the field locations, crop rotation contributed differently to the variation in microbial community composition. Strong differences were observed in communities inhabiting soils under monosuccession (RR vs. MM) (ANOSIM: R = 0.923 for the bacterial and R = 0.714 for the fungal community), while the communities in soils undergoing MR crop rotation were more similar to those of the corresponding RR soils (ANOSIM: R = 0.111–0.175). The observed differences could be explained by altered oxygen availabilities in RR and MR soils, resulting in an enrichment of anaerobic bacteria in the soils, and the presence of the different crops, leading to the enrichment of host-plant specific microbial communities. The responses of the microbial communities to the application of rice straw in the microcosms were rather weak compared to the other factors. The taxa responding in bulk soil and rhizosphere were mostly distinct. In conclusion, this study revealed that the different agricultural management practices affect microbial community composition to different extent, not only in the bulk soil but also in the rhizosphere, and that the microbial responses in bulk soil and rhizosphere are distinct.</p

Data_Sheet_2_Crop Rotation and Straw Application Impact Microbial Communities in Italian and Philippine Soils and the Rhizosphere of Zea mays.xlsx

<p>Rice is one of the most important nourishments and its cultivation binds large agricultural areas in the world. Its cultivation leads to huge water consumption and high methane emissions. To diminish these problems, crop rotation between paddy rice and maize is introduced in Asia, but can lead to losses of carbon and water by the formation of desiccation cracks. To counteract these problems rice straw can be applied. We analyzed soil microbial responses to different crop rotation systems [rice–rice (RR), maize–maize (MM), maize–rice (MR)] and to rice straw application in the soil and rhizosphere of maize. Zea mays was grown in microcosms using soils from different field locations, each including different crop rotation regimes. The bacterial and fungal community composition was analyzed by 16S rRNA gene and ITS based amplicon sequencing in the bulk soil and rhizosphere. The microbiota was clearly different in soils from the different field locations (analysis of similarity, ANOSIM: R = 0.516 for the bacterial community; R = 0.817 for the fungal community). Within the field locations, crop rotation contributed differently to the variation in microbial community composition. Strong differences were observed in communities inhabiting soils under monosuccession (RR vs. MM) (ANOSIM: R = 0.923 for the bacterial and R = 0.714 for the fungal community), while the communities in soils undergoing MR crop rotation were more similar to those of the corresponding RR soils (ANOSIM: R = 0.111–0.175). The observed differences could be explained by altered oxygen availabilities in RR and MR soils, resulting in an enrichment of anaerobic bacteria in the soils, and the presence of the different crops, leading to the enrichment of host-plant specific microbial communities. The responses of the microbial communities to the application of rice straw in the microcosms were rather weak compared to the other factors. The taxa responding in bulk soil and rhizosphere were mostly distinct. In conclusion, this study revealed that the different agricultural management practices affect microbial community composition to different extent, not only in the bulk soil but also in the rhizosphere, and that the microbial responses in bulk soil and rhizosphere are distinct.</p

Predicting microbiomes through a deep latent space

MOTIVATION: Microbial communities influence their environment by modifying the availability of compounds, such as nutrients or chemical elicitors. Knowing the microbial composition of a site is therefore relevant to improve productivity or health. However, sequencing facilities are not always available, or may be prohibitively expensive in some cases. Thus, it would be desirable to computationally predict the microbial composition from more accessible, easily-measured features. RESULTS: Integrating deep learning techniques with microbiome data, we propose an artificial neural network architecture based on heterogeneous autoencoders to condense the long vector of microbial abundance values into a deep latent space representation. Then, we design a model to predict the deep latent space and, consequently, to predict the complete microbial composition using environmental features as input. The performance of our system is examined using the rhizosphere microbiome of Maize. We reconstruct the microbial composition (717 taxa) from the deep latent space (10 values) with high fidelity (>0.9 Pearson correlation). We then successfully predict microbial composition from environmental variables, such as plant age, temperature or precipitation (0.73 Pearson correlation, 0.42 Bray–Curtis). We extend this to predict microbiome composition under hypothetical scenarios, such as future climate change conditions. Finally, via transfer learning, we predict microbial composition in a distinct scenario with only 100 sequences, and distinct environmental features. We propose that our deep latent space may assist microbiome-engineering strategies when technical or financial resources are limited, through predicting current or future microbiome compositions. AVAILABILITY AND IMPLEMENTATION: Software, results and data are available at https://github.com/jorgemf/DeepLatentMicrobiome SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online