PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 16825475

Long-term cropping system effects on carbon sequestration in eastern Oregon.

Stephen Machado·Karl Rhinhart·Steve Petrie
View original source ↗Export metadata ↓

Abstract

Soil organic carbon (SOC) has beneficial effects on soil quality and productivity. Cropping systems that maintain and/or improve levels of SOC may lead to sustainable crop production. This study evaluated the effects of long-term cropping systems on C sequestration. Soil samples were taken at 0- to 10-, 10- to 20-, 20- to 30-, and 30- to 40-cm soil depth profiles from grass pasture (GP), conventional tillage (CT) winter wheat (Triticum aestivum L.)-fallow (CTWF), and fertilized and unfertilized plots of continuous winter wheat (WW), spring wheat (SW), and spring barley (Hordeum vulgare L.) (SB) monocultures under CT and no-till (NT). The samples were analyzed for soil organic matter (SOM) and SOC was derived. Ages of experiments ranged from 6 to 73 yr. Compared to 1931 SOC levels (initial year), CTWF reduced SOC by 9 to 12 Mg ha(-1) in the 0- to 30-cm zone. Grass pasture increased SOC by 6 Mg ha(-1) in the 0- to 10-cm zone but decreased SOC by 3 Mg ha(-1) in the 20- to 30-cm zone. Continuous CT monocultures depleted SOC in the top 0- to 10-cm zone and the bottom 20- to 40-cm zone but maintained SOC levels close to 1931 SOC levels in the 10- to 20-cm layer. Continuous NT monocultures accumulated more SOC in the 0- to 10-cm zone than in deeper zones. Total SOC (0- to 40-cm zone) was highest under GP and continuous cropping and lowest under CTWF. Fertilizer increased total SOC only under CTWW and CTSB by 13 and 7 Mg ha(-1) in 13 yr, respectively. Practicing NT for only 6 yr had started to reverse the effect of 73 yr of CTWF. Compared to CTWF, NTWW and NTSW sequestered C at rates of 2.6 and 1.7 Mg ha(-1) yr(-1), respectively, in the 0- to 40-cm zone. This study showed that the potential to sequester C can be enhanced by increasing cropping frequency and eliminating tillage.

Explore related subjects

AgricultureBiomassCarbonConservation of Natural ResourcesCrops, AgriculturalEnvironmental MonitoringFertilizersGeographyOregonOrganic ChemicalsSoilComparative StudyJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Stephen Machado, Karl Rhinhart, Steve Petrie. 2006-07-06. Long-term cropping system effects on carbon sequestration in eastern Oregon.. https://doi.org/10.2134/jeq2005.0201

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Plant-derived and microbial biostimulants in sustainable agriculture: mechanisms, applications, and challenges.

Plant biostimulants have emerged as transformative and sustainable tools for improving crop productivity, resource-use efficiency, and resilience under rapidly intensifying environmental stresses. Unlike conventional agrochemicals, biostimulants function by activating physiological, biochemical, and molecular processes that optimize plant performance without directly supplying nutrients or exerting pesticidal effects. This review comprehensively examines the integrated roles of plant-derived and microbial biostimulants in sustainable agriculture, with particular emphasis on microbial-mediated mechanisms underlying plant stress adaptation and rhizosphere functioning. Plant-derived biostimulants, including seaweed extracts, humic substances, protein hydrolysates, amino acids, and chitosan, enhance nutrient acquisition, root architecture, hormonal regulation, and antioxidant defense systems. More importantly, microbial biostimulants, such as plant growth-promoting rhizobacteria (PGPR), endophytic microorganisms, mycorrhizal fungi, actinomycetes, yeasts, and cyanobacteria, exert multifunctional effects through biological nitrogen fixation, mineral solubilization, phytohormone biosynthesis, volatile signaling, osmolyte accumulation, pathogen suppression, and modulation of stress-responsive genes. These beneficial microorganisms reshape rhizosphere microbial communities, improve nutrient cycling, and enhance plant tolerance to drought, salinity, heat, and heavy metal toxicity. Emerging evidence from genomics, transcriptomics, metabolomics, and microbiome-based investigations has further revealed the molecular networks and signaling pathways governing biostimulant-induced resilience and plant-microbe interactions. Despite their substantial promise, inconsistent field performance, formulation instability, regulatory limitations, and inadequate mechanistic understanding continue to restrict their large-scale adoption. This review highlights recent advances in microbial and plant-derived biostimulants while identifying critical knowledge gaps and future opportunities for precision biostimulant engineering, microbiome manipulation, and climate-resilient crop management. The integration of next generation biostimulant technologies into sustainable agricultural systems may significantly reduce dependence on agrochemicals while improving crop productivity, environmental sustainability, and global food security.

Agriculture↗
PubMed2026

Data-driven approaches in green microbiology: strategies for plant growth-promoting bacteria.

Plant growth-promoting bacteria (PGPB) are gaining attention as scalable biological solutions to enhance crop productivity and resilience. However, accurately identifying and characterizing PGPB remains challenging, particularly under variable environmental conditions where microbial functions are context-dependent and shaped by complex plant-microbe interactions. Advances in high-throughput sequencing have shifted the field from culture-dependent approaches to genome-informed strategies, enabling large-scale taxonomic and functional profiling. Although trait-based databases support the prediction of plant-beneficial genes, they capture only a fraction of the underlying biological complexity and often require labor-intensive analyses. Machine learning (ML) and deep learning (DL) have emerged as powerful tools to integrate genomic, physiological, and ecological data, enabling the prioritization of candidate strains with plant growth-promoting potential. To evaluate advances in the field, we conducted a systematic review of studies integrating ML and DL with PGPB characterization, assessing algorithm selection, performance, and target plant systems. Across 248 observations, only 6.0% of studies directly addressed PGPB screening, whereas the majority (77.4%) focused on plant disease detection, revealing a substantial gap in the application of AI to beneficial microorganisms for plant growth. Convolutional neural networks (CNNs) were the most frequently applied algorithms, largely driven by image-based phenotyping tasks. Overall, the field is constrained by limited datasets, high computational demands, and challenges in modeling multispecies and host-associated interactions. We highlight the need for integrative and interpretable ML and DL frameworks that bridge genomic data and functional validation. Such approaches represent a promising path toward scalable, data-driven discovery and deployment of bioinoculants in sustainable agriculture.

Agriculture↗
PubMed2026

Artificial intelligence-driven advancements in agricultural biotechnology.

The need for faster and more informative data processing for better decision-making is driving the adoption of artificial intelligence (AI) in the agricultural sector. Thanks to recent advancements in computer science and the increase in computational powers of modern computers, AI is not only augmenting traditional solutions, but also helping in developing novel solutions to existing challenging matters. AI-driven models have an exceptional ability to identify patterns and combine a diverse collection of data together and make inference. The increasing pressure on farmlands posed by the growing global population and climate change is lessening growth, yield, and productivity ultimately posing risk to food security worldwide. Incorporation of AI in agriculture has the potential to drive farming efficiency to new heights. This comprehensive review critically evaluates the evolution of AI in agricultural biotechnology from a theoretical concept to a global phenomenon. A comprehensive literature search was performed using major scientific databases, including PubMed, Web of Science, Embase, Scopus, Lens and the Cochrane Library. In this review, we empirically demonstrate the fields advancement toward more capable AI systems and discuss the current applications of AI across crop improvement and precision agriculture such as crop improvement and genetic engineering, genomic selection and plant breeding, pest and disease detection, precision agriculture and smart farming, soil health and nutrient management, climate resilient crop development, livestock biotechnology, challenges and ethical considerations in AI based agricultural biotechnology. Furthermore, this review addresses the exponential growth of commercial intellectual property in the field and contrast it with academic publication outputs. Finally, we critically assess the ethical challenges impeding equitable adoption of AI including data sovereignty and digital divide, while projecting future frontiers involving quantum computing. This review will help build sustainable agricultural systems capable of adapting to climate change, contribute to the development of climate-resilient and high-yielding crops, and address global food security challenges.

Agriculture↗