The biogeography of microbial N cycle guilds of the rye rhizosphere along a tillage erosion catena DOI Creative Commons
Simon Lewin,

Marc Wehrhan,

Sonja Wende

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: July 25, 2024

Abstract Background Excessive fertilization and tillage erosion pose threats to food security crop yields. A transition towards more sustainable agricultural practices may be advanced by harnessing ecosystem services provided plant microbiomes. However, targeting microbiota at the agroecosystem scale necessitates bridging gap micro-scale structures of We hypothesized, that relevant changes microbial N cycle guilds in rhizosphere rye align with a soil catena determined erosion. Aboveground patterns biomass along such persist hummocky landscapes are practical relevance farmers. Results The topsoil four typical soils an arable field grown within Quillow catchment (NE Germany) was sampled. represent complete gradient from extremely eroded Calcaric Regosol over strongly Nudiargic Luvisol non-eroded Calcic colluvial Gleyic-Colluvic Regosols. Gene abundances characteristic were analysed using shotgun metagenomic sequencing. Distinct growth plants correlated nitrogen functions microbiome based on multivariate analyses. ratios describing differential denitrification potential differed significantly between soils. norBC gene abundance most coupled productivity, which is likely due its involvement into multiple interactions besides denitrification. Genes associated DNRA diazotrophy prevailed sites showed lowest productivity mineral availability. Additionally, limitation implied lowered gdh to glnA ratio association compared depositional site. Conclusions Thus, gradients legacy management as capture substantial functionality. These specific assembly function above ground field-plant accessible remote sensing. interrelation in-field opens up opportunity assess distribution functional scales production agroecosystems functioning.

Language: Английский

Enhancing intercropping sustainability: Manipulating soybean rhizosphere microbiome through cropping patterns DOI

Pengfei Dang,

Lu Chen,

Tiantian Huang

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 931, P. 172714 - 172714

Published: April 26, 2024

Language: Английский

Citations

8
Conversion of monocropping to intercropping promotes rhizosphere microbiome functionality and soil nitrogen cycling DOI
Duntao Shu, Samiran Banerjee,

Xinyi Mao

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 949, P. 174953 - 174953

Published: July 26, 2024

Language: Английский

Citations

7
How to get to the N – a call for interdisciplinary research on organic N utilization pathways by plants DOI Creative Commons
Maire Holz, Simon Lewin, Steffen Kolb

et al.

Plant and Soil, Journal Year: 2024, Volume and Issue: unknown

Published: July 15, 2024

Abstract Background and aims While nitrogen (N) derived from soil organic matter significantly sustains agricultural plants, the complexities of N utilization pathways remain poorly understood. Knowledge gaps persist regarding diverse pools, microbial processes in mineralization, how plants shape N-mineralizing community through root exudation. Results To address these gaps, we propose an integrated conceptual framework that explores intricate interplay soil, plant, microbiome dynamics within context carbon (C) cycling. Emphasizing plant effects on gross depolymerization deamination N—a crucial yet often overlooked aspect—we aim to enhance our understanding pathways. In this context, suggest considering linkages between hyphal exudation, followed by rhizosphere priming which turn control mobilization. Based relation exudation turnover, identify necromass as a potentially important source for plants. Furthermore, applying economic theory gain insights into strategies employed accessing N. Stable isotope tracers functional analytics provide tools decipher complex network utilization. Conclusions The envisioned holistic pathways, intricately connects microorganisms. This lays groundwork sustainable practices, reducing losses.

Language: Английский

Citations

4
Synergistic integration of remote sensing and soil metagenomics data: advancing precision agriculture through interdisciplinary approaches DOI Creative Commons
Bindu Ambaru,

Reena Manvitha,

Rajini Madas

et al.

Frontiers in Sustainable Food Systems, Journal Year: 2025, Volume and Issue: 8

Published: Jan. 6, 2025

The global demand for food is driving the need high-performance, sustainable agricultural systems that incorporate advanced technologies monitoring, control, and decision-making. With population expected to reach 9.7 billion by 2050, agriculture must boost productivity while maintaining sustainability. Precision (PA) addresses this challenge using increase yields, reduce resource waste, minimize environmental impacts (Gebbers Adamchuk, 2010; Delgado, Sassenrath Mueller, 2020; El-Kader El-Basioni, 2020). This "fourth revolution" reshaping farming through innovations in data analytics, communication, technology (Mohindru et al., 2021; Abdel-Basset, Hawash Abdel-Fatah, 2024).A key aspect of soil microbiome, especially rhizosphere, which promotes health crop resilience reducing harm. Next-generation sequencing (NGS) techniques, such as amplicon shotgun metagenomics, provide deep insights into microbial communities, their diversity, functional roles. These tools are vital monitoring interventions, identifying beneficial microbes, detecting pathogens early prevent diseases (Elnahal 2022).Understanding physical, biological, chemical characteristics crucial optimizing management practices irrigation, drainage, nutrient management—key components PA. integration like artificial intelligence (AI), remote sensing, unmanned aerial vehicles (UAVs), big Internet Things (IoT), Global Positioning system (GPS), Geographic Information Systems (GIS) enables precise spatial variability fields. UAVs, with high resolution flexibility, have revolutionized offering real-time collection from difficult-to-reach areas (Boursianis 2022).Integrating UAV-based sensing metagenomics represents a transformative step forward PA ecosystem restoration. fusion these not only enhances improving efficiency but also aligns broader objectives agriculture, impact minimizing inputs fostering healthier, more resilient ecosystems. As evolve become cost-effective accessible, will likely standard practice modern widespread adoption promoting future production. We review current advancements both fields, propose methods integrating microbiome profiles, present framework implementing integrated approach optimize precision farming.Soil MetagenomicsSoil diverse environment, home billions microorganisms. Enhancing can 10-50%, plant growth-promoting rise 50-60% (Abram, 2015; O'Callaghan, Ballard Wright, 2022). reduces reliance on fertilizers, supporting agriculture. Metagenomics, sequences analyzes DNA, reveals diversity aids discovering therapeutic molecules, biotechnological innovations, Garrido-Oter 2018). It offers community structures, including bacteria, archaea, eukaryotes, based gene composition (Philippot 2013; Martínez-Porchas Vargas-Albores, 2017). workflow be discussed detail further.Soil sampling, library preparation sequencing: Metagenomic studies involve collecting samples, particularly where microbes root secretions interact (Weaver 1994; Brooks 2015). Total DNA extracted samples kits Genejet Soil Kit (Thermo Fisher) or Fast SPIN (MP Biochemicals). then enzymatically fragmented Nextera Tagmentation (Illumina) Fragmentase (New England Biolabs), alternative acoustic shearing, sonication, others (N. Sabale, P. Suryawanshi Krishnaraj, concentration purity measured Qubit Nanodrop, integrity assessed via agarose gel electrophoresis Agilent TapeStation. fragments cloned bacterial plasmids, featuring elements an origin replication, restriction sites, selective markers, cloning sites (Granjou Phillips, 2019). Fragments analyzed fragment analyzer quality quantity. Sequencing conducted platforms Illumina, Pyrosequencing, Nanopore, PacBio. Post-sequencing, de-multiplexed (Martin, 2011; Oulas Mahmoud 2019; Zhang 2021).Data processing: Pre-processing begins control filter out low-quality reads remove adapter sequences. Tools UCHIME, MG-RAST, RDP (Bolger, Lohse Usadel, 2014), KTrim, Trim Galore, Trimmomatic (Sun, 2020) utilized tasks, ensuring trimmed uniform length quality. Following trimming, further filtered exclude below specified thresholds, errors corrected polymerase chain reaction (PCR) duplicates removed enhance accuracy. Denoising metagenomic achieved MOTHUR QIIME 2, UCHIME used eliminating chimeric (Santamaria Post-processing involves grouping unique barcodes, removing primers, employing Taxator-tk (Dröge, Gregor McHardy, 2015) MEGAHIT (Liu taxonomic classification. Recent de novo assemblers Meta-IDBA, metaSPAdes, Ray Meta, Contig Extender allow assembly contigs, novel genomes without prior reference (Peng Boisvert 2012; Kumar 2018; Deng Delwart, 2021). Subsequently, aligned databases assembled contigs comprehensive analysis, facilitating robust interpretation data.Data Analysis interpretation: processing forms basis profiling, essential understanding communities soil. Krona, MEGAN, phyloseq R visualize abundance (Huson 2007; Ondov, Bergman Phillippy, McMurdie Holmes, 2013). Functional analysis prediction Prodigal MetaGeneMark, followed annotation KEGG, COG, Pfam eggNOG-mapper InterProScan (Hyatt Zhu, Lomsadze Borodovsky, 2010). Pathway reconstruction KEGG MetaCyc HUMAnN2 (Franzosa 2018) PathoScope (Hong capabilities predicted PICRUSt, Tax4Fun (Langille Sun, Jones Fodor, Statistical analyses, DESeq2, edgeR, PCA, NMDS, identify differential taxa functions. Integration data, along network ecological models, explores interactions roles (Robinson, McCarthy Smyth, 2010).Nutrient plan: Common species isolated rhizosphere approaches include Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, (Babalola, Santos, Nogueira Hungria, Prasad Zhang, Table 1 highlights By fix nitrogen solubilize phosphorus, farmers synthetic enhancing productivity, profits, sustainability (Mendes, Garbeva Raaijmakers, Enzymes sulfatases, dehydrogenases, phosphatases improve fertility, growth, yield, pesticide use Metagenomics supports development biofertilizers inoculants alternatives traditional remediation 2013, 2024). Several demonstrated economic advantages For instance, recurrent pre-sowing applications Pseudomonas fluorescens significantly boosted maize costly fertilizers (Papin al. Investigations microbiota responses nitric oxide regulation Arabidopsis thaliana highlight potential optimized plant-microbe (Berger Moreover, trials affordable solutions underscored role security conventional (van der Velde 2013).Remote sensingThe appropriate spatio-temporal required depends various factors, objectives, field size, capability farm equipment vary input (irrigation, fertilizer, pesticide, etc.) application rates. While variety sensors, paper limits itself those primarily UAV image (Shafi UAVs transforming precise, efficient, practices. They help conserving resources, eco-friendly predict 2025, industry would at compound annual growth rate 35.9% $5.7 (Agriculture Drones Market, no date).Drones cameras: Aerial drones generally higher (<5 meters) images compared satellites (Bochtis, Tagarakis Kateris, 2023). Thus, other ground-based offer greater flexibility providing fine temporal resolutions (more frequently) needed. hydrologic climatic parameters—such organic carbon, moisture, characteristics, normalized Difference Vegetation Index (NDVI), leaf area index (LAI), groundwater, rainfall—as well vegetation monitored (UAVs) (Zhang Equipped perform specialized tasks: multispectral sensors capture specific light wavelengths, thermal cameras detect temperature changes irrigation pest issues, LIDAR creates detailed topographic maps land water (Maddikunta Tahir However, drone varies due differing legal, financial, physical conditions across countries. Supplementary S1 lists recognized improved management, compliance safety operational standards respective aviation authorities.Data Pre-processing: Data preprocessing IoT creating dataset. Decagon EC-5 soil, Davis Vantage Pro2 weather, GreenSeeker crops, gather temperature, pH, nutrients, climate, (García Fuentes Chang, S2 employed emphasizing importance enabling real-time, data-driven solutions. Connectivity networks LoRaWAN, NB-IoT, 5G transmit gateways Kerlink Wirnet Station. Flight planning software DJI Ground Station Pro Phantom 4 RTK (Križanović 2023), georeferencing stitching Agisoft Metashape. Noise reduction Pix4Dmapper quality, cloud computing secure storage (Debauche 2022).Data Analytics AI: widely adopted power AI, driven machine learning (ML) (DL) (Khan, Khan Ansari, 2022; Ojo Zahid, Hashmi Kesakr, ML models analyze UAV-captured images, AI-enabled sensor recommendations farmers. Machine Support Vector Machines (SVM) Random Forests were applied drying patterns (Liakos Sharma Models utilizing Decision Trees Neural Networks created pH fertility (Suchithra Pai, 2020), Multiple Linear Regression (MLR) (SVR) estimate Organic Matter (SOM) paddy soils (Yang Partial Least Squares (PLSR) was moisture content (MC), total (TN), carbon (SOC) (Morellos 2016). estimated combining Auto-Regressive Error Function (AREF) Gradient Boosting k-Nearest Neighbors (k-NN) (Johann Extreme Learning (ELM) Self-Adaptive Evolutionary agent (SaE) assess (Nahvi 2016), ELM forecast surface humidity (Acar, Ozerdem Ustundag, Lastly, SVM SOC TN Moroccan (Reda recognition techniques seed sorting counting (Li Nehoshtan Laudari, Marks Rognon, Ekramirad Deep Learning, Convolutional (CNNs), disease detection, allowing rapid accurate diagnosis, loss (Mohanty, Hughes Salathé, 2016; Ramcharan 2017; (Too Argüeso processes planting, fertilization, productivity. 2 parameters detected showcasing research. AI refine recommending amounts water, pesticides, waste impact. simulate different scenarios, helping select most effective strategies (Marvuglia 2022).Actionable Insights: Complex simplified actionable farmers, delivered mobile apps dashboards, updates, visualizations, alerts. receive notifications about outbreaks suggested treatments alerts sudden drops advice. Farmers implement feedback, helps system's accuracy relevance over time. feedback loop, combined adaptive learning, predictive growing seasons. benefits operations increased yields timely efficient allocation. empowers make informed decisions, boosting profitability, agriculture.Integrating AgricultureVarious Remote been monitor biodiversity landscapes (Herzog Franklin, Lewin combination exascale computing, multi-omics biology research UN's Sustainable Development Goals (Streich Cembrowska-Lech Integrating imaging links dynamics (Beatty Singer Soil-plant-microbiota interactions, health, emphasized (Giovannetti Dlamini, Sekhohola-Dlamini Cowan, (Meena 2024; Zeng 2024), AI-driven advance forest cycling, drought tolerance crops (Chaudhury Jamil Additionally, omics phytoremediation outcomes (Mohan illustrated Figure 1, it clear promote datasets within interdisciplinary promising pathway forward, poses significant challenges complexity harmonizing scales, complexities, requiring multivariate statistical network-based Ensuring model interpretability another hurdle, many AI/ML function "black boxes," complicating biological limiting trust predictions. Overfitting, common issue ML, undermines generalizability automating uncovering non-linear collaboration demanding tasks require expertise innovation. Addressing leveraging full agriculture.Future DirectionThe hinges integrates Omics all grounded One Health concept, emphasizes interconnectedness human, plant, health. Collaboration among experts molecular biology, microbiology, ecology, bioinformatics, computer science managing complex datasets, efficiency, farming. In-field drones, (e.g., Oxford Nanopore's MinION) enable immediate conditions, delivering directly mobile-friendly, easy-to-understand reports. advances promise low-intervention, automated sophisticated algorithms process instantly, use, respond real time, yields. Despite around complexity, technical expertise, integration, privacy, security, cost limitations, technology-driven holds sustainability, system.

Language: Английский

Citations

0
How does the long-term return of mix-sowing green manures increase nitrogen utilization and decrease ecological costs of wheatland under reduced chemical nitrogen input? DOI Creative Commons
Jingui Wei, Wen Yin,

Qiang Chai

et al.

Resources Environment and Sustainability, Journal Year: 2025, Volume and Issue: unknown, P. 100220 - 100220

Published: March 1, 2025

Language: Английский

Citations

0
The microbial-driven nitrogen cycle and its relevance for plant nutrition DOI
Hanna Koch, Angela Sessitsch

Journal of Experimental Botany, Journal Year: 2024, Volume and Issue: 75(18), P. 5547 - 5556

Published: June 18, 2024

Abstract Nitrogen (N) is a vital nutrient and an essential component of biological macromolecules such as nucleic acids proteins. Microorganisms are major drivers N-cycling processes in all ecosystems, including the soil plant environment. The availability N growth-limiting factor for plants it significantly affected by microbiome. Plants microorganisms form complex interaction networks resulting molecular signaling, exchange, other distinct metabolic responses. In these networks, microbial partners influence growth use efficiency either positively or negatively. Harnessing beneficial effects specific players within crop microbiomes promising strategy to counteract emerging threats human planetary health due overuse industrial fertilizers. However, addition N-providing activities (e.g. well-known symbiosis legumes Rhizobium spp.), plant–microorganism interactions must be considered obtain complete picture how microbial-driven transformations might affect nutrition. For this, we review recent insights into tight interplay between microorganisms, focusing on N-transformation representing sources sinks that ultimately shape acquisition.

Language: Английский

Citations

3
Belowground microbiota associated with the progression of Verticillium wilt of smoke trees DOI

Ruifeng Guo,

Bimeng Li, Qiyan Li

et al.

Plant and Soil, Journal Year: 2024, Volume and Issue: 500(1-2), P. 515 - 529

Published: Feb. 3, 2024

Language: Английский

Citations

2
Exploring microbial diversity in the rhizosphere: a comprehensive review of metagenomic approaches and their applications DOI
Bhumi Rajguru, Manju Shri, Vaibhav D. Bhatt

et al.

3 Biotech, Journal Year: 2024, Volume and Issue: 14(10)

Published: Sept. 6, 2024

Language: Английский

Citations

2
Respuesta espectral del cultivo del maíz aplicando dosis diferenciadas de fertilización DOI Creative Commons

Roger Adrián Delgado Alcívar,

Henry Pacheco, Ezequiel Zamora-Ledezma

et al.

Revista Científica Multidisciplinaria SAPIENTIAE, Journal Year: 2024, Volume and Issue: 7(13), P. 60 - 70

Published: April 15, 2024

La investigación llevada a cabo en Santa Ana, Ecuador, busca correlacionar el Índice de Vegetación Diferencia Normalizada (NDVI) con Clorofila Verde (GCI) diferentes estados fenológicos del maíz, aprovechando la teledetección través vuelos fotogramétricos utilizando dron eBee. Los resultados exhiben una relación positiva entre GCI y NDVI todas las fases evaluadas crecimiento cultivo, coeficientes determinación (R²) destacados: 0,9138 estado V5, 0,8912 V11, 0,8461 VT (floración). Estos valores respaldan eficacia como indicador confiable salud contenido clorofila pesar ligeras variaciones según etapa desarrollo. hallazgos enriquecen conocimiento científico proporcionan perspectivas valiosas para implementar gestión agrícola sostenible toma decisiones informadas producción agrícola.

Citations

1
Fostering Microbial Activity and Diversity in Agricultural Systems DOI
Om Prakash Ghimire,

Ariana Lazo,

Binaya Parajuli

et al.

CSA News, Journal Year: 2024, Volume and Issue: 69(6), P. 43 - 47

Published: May 24, 2024

Language: Английский

Citations

1