25 research outputs found

    Evaluation of residue management practices on barley residue decomposition

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    Optimizing barley (hordeum vulgare L.) production in Idaho and other parts of the Pacific Northwest (PNW) should focus on farm resource management. The effect of post-harvest residue management on barley residue decomposition has not been adequately studied. Thus, the objective of this study was to determine the effect of residue placement (surface vs. incorporated), residue size (chopped vs. ground-sieved) and soil type (sand and sandy loam) on barley residue decomposition. A 3-mo laboratory incubation experiment was conducted at a temperature of 25 to 30 °C at the Aberdeen Research and Extension Center, Aberdeen, Idaho, USA. Following the study, a Markov-Chain Monte Carlo (MCMC) modeling approach was applied to investigate the first-order decay kinetics of barley residue. An accelerated initial flush of C-mineralization was measured for the sieved (Day 1) compared to chopped (Day 3 to 5) residues for both surface incorporated applications. The highest evolution of CO2-C of 8.3 g kg-1 was observed on Day 1 from the incorporated-sieved application for both soils. The highest and lowest amount of cumulative CO2-C released and percentage residue decomposed over 50-d was observed for surface-chopped (107 g kg-1 and 27%, respectively) and incorporated-sieved (69 g kg-1 and 18%, respectively) residues, respectively. There were no significant differences in C-mineralization from barley residue based on soil type or its interactions (p >0.05). The largest decay constant k of 0.0083 d-1 was calculated for surface-chopped residue where the predicted half-life was 80 d, which did not differ from surface sieved or incorporated chopped. In contrast, incorporated-sieved treatments only resulted in a k of 0.0054 d-1 and would need an additional 48 d to decompose 50% of the residue. Future residue decomposition studies under field conditions are warranted to verify the residue C-mineralization and its impact on residue management

    Determination of suitable extractant for estimating plant available arsenic in relation to soil properties and predictability by solubility-FIAM

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    Extractant for estimating plant available arsenic (As) in soil has not been universally established. Moreover, to assess and monitor the complex chemical behaviour of arsenic (As) in soil and subsequently its transfer in crops, a suitable extraction protocol considering the soil properties in relation to crop uptake is required. For this purpose, a pot experiment was conducted to evaluate the suitability of the extractants for determination of extractable As in soil and risk assessment by solubility-free ion activity model (FIAM) with rice (variety: Sushk Samrat) as the test crop. Soil in bulk was collected from six locations of Indo-Gangetic Plain of Bihar, India, varying in physicochemical properties to conduct the pot experiment using five doses of As (0, 10, 20, 40 and 80 mg kg−1). Six extractants namely 0.2 (M) NH4-Oxalate, 0.05 (N) HCl + 0.025 (N) H2SO4, 0.5 (M) KH2PO4, 0.5 (N) NH4F, 0.5 (M) NaHCO3 and 0.5 (M) EDTA were used. The results revealed that 0.5 (M) KH2PO4 gave the best correlation with the soil properties and crop uptake and can be considered as a suitable extractant of As. Regardless of the As dose and the soil type used, in rice tissue, concentration of As followed the order root > straw > leaf and grain. As high as 94% variation in As content in rice grain could be explained, when 0.5 (M) KH2PO4 extractable As is being used as input for solubility-FIAM. Extractable As cannot be determined by Atomic Absorption Spectrophotometer (AAS) coupled with Vapour Generation Accessory (VGA) when 0.5 (M) EDTA was used as an extractant

    Modeling Denitrification : Can We Report What We Don't Know?

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    Funding Information: This study is the products of a workshop funded by the Deutsche Forschungsgemeinschaft through the research unit DFG‐FOR 2337: Denitrification in Agricultural Soils: Integrated Control and Modelling at Various Scales (DASIM), and by the German Federal Ministry of Education and Research (BMBF) under the “Make our Planet Great Again—German Research Initiative”, Grant 306060, implemented by the German Academic Exchange Service (DAAD). This work was supported by the European Union's Horizon 2020 research and innovation programme project VERIFY (grant agreement no. 776810). We would like to thank the contribution of all workshop participants of the II. DASIM Modeler Workshop. Publisher Copyright: © 2023. The Authors.Peer reviewedPublisher PD

    Reanalysis in Earth System Science: Towards Terrestrial Ecosystem Reanalysis

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    A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modelled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyses, and more in detail biogeochemical ocean and terrestrial reanalyses. In particular, we identify land surface, hydrologic and carbon cycle reanalyses which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic-abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics

    Toward a Generalizable Framework of Disturbance Ecology Through Crowdsourced Science

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    © 2021 Graham, Averill, Bond-Lamberty, Knelman, Krause, Peralta, Shade, Smith, Cheng, Fanin, Freund, Garcia, Gibbons, Van Goethem, Guebila, Kemppinen, Nowicki, Pausas, Reed, Rocca, Sengupta, Sihi, Simonin, Słowiński, Spawn, Sutherland, Tonkin, Wisnoski, Zipper and Contributor Consortium.Disturbances fundamentally alter ecosystem functions, yet predicting their impacts remains a key scientific challenge. While the study of disturbances is ubiquitous across many ecological disciplines, there is no agreed-upon, cross-disciplinary foundation for discussing or quantifying the complexity of disturbances, and no consistent terminology or methodologies exist. This inconsistency presents an increasingly urgent challenge due to accelerating global change and the threat of interacting disturbances that can destabilize ecosystem responses. By harvesting the expertise of an interdisciplinary cohort of contributors spanning 42 institutions across 15 countries, we identified an essential limitation in disturbance ecology: the word ‘disturbance’ is used interchangeably to refer to both the events that cause, and the consequences of, ecological change, despite fundamental distinctions between the two meanings. In response, we developed a generalizable framework of ecosystem disturbances, providing a well-defined lexicon for understanding disturbances across perspectives and scales. The framework results from ideas that resonate across multiple scientific disciplines and provides a baseline standard to compare disturbances across fields. This framework can be supplemented by discipline-specific variables to provide maximum benefit to both inter- and intra-disciplinary research. To support future syntheses and meta-analyses of disturbance research, we also encourage researchers to be explicit in how they define disturbance drivers and impacts, and we recommend minimum reporting standards that are applicable regardless of scale. Finally, we discuss the primary factors we considered when developing a baseline framework and propose four future directions to advance our interdisciplinary understanding of disturbances and their social-ecological impacts: integrating across ecological scales, understanding disturbance interactions, establishing baselines and trajectories, and developing process-based models and ecological forecasting initiatives. Our experience through this process motivates us to encourage the wider scientific community to continue to explore new approaches for leveraging Open Science principles in generating creative and multidisciplinary ideas.This research was supported by the U.S. Department of Energy (DOE), Office of Biological and Environmental Research (BER), as part of Subsurface Biogeochemical Research Program’s Scientific Focus Area (SFA) at the Pacific Northwest National Laboratory (PNNL). PNNL is operated for DOE by Battelle under contract DE-AC06-76RLO 1830

    Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community

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    It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building

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    Comparing models of microbial–substrate interactions and their response to warming

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    Recent developments in modelling soil organic carbon decomposition include the explicit incorporation of enzyme and microbial dynamics. A characteristic of these models is a positive feedback between substrate and consumers, which is absent in traditional first-order decay models. With sufficiently large substrate, this feedback allows an unconstrained growth of microbial biomass. We explore mechanisms that curb unrestricted microbial growth by including finite potential sites where enzymes can bind and by allowing microbial scavenging for enzymes. We further developed a model where enzyme synthesis is not scaled to microbial biomass but associated with a respiratory cost and microbial population adjusts enzyme production in order to optimise their growth. We then tested short- and long-term responses of these models to a step increase in temperature and find that these models differ in the long-term when short-term responses are harmonised. We show that several mechanisms, including substrate limitation, variable production of microbial enzymes, and microbes feeding on extracellular enzymes eliminate oscillations arising from a positive feedback between microbial biomass and depolymerisation. The model where enzyme production is optimised to yield maximum microbial growth shows the strongest reduction in soil organic carbon in response to warming, and the trajectory of soil carbon largely follows that of a first-order decomposition model. Modifications to separate growth and maintenance respiration generally yield short-term differences, but results converge over time because microbial biomass approaches a quasi-equilibrium with the new conditions of carbon supply and temperature
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