scholarly journals Quantifying soil carbon in temperate peatlands using a mid-IR soil spectral library

SOIL ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 193-215
Author(s):  
Anatol Helfenstein ◽  
Philipp Baumann ◽  
Raphael Viscarra Rossel ◽  
Andreas Gubler ◽  
Stefan Oechslin ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Scale Up ◽  
Soil Characteristics ◽  
Partial Least Square ◽  
Total Carbon ◽  
Local Models ◽  
Spectral Library ◽  
Data Set ◽  
Space And Time ◽  
Soil Information

Abstract. Traditional laboratory methods for acquiring soil information remain important for assessing key soil properties, soil functions and ecosystem services over space and time. Infrared spectroscopic modeling can link and massively scale up these methods for many soil characteristics in a cost-effective and timely manner. In Switzerland, only 10 % to 15 % of agricultural soils have been mapped sufficiently to serve spatial decision support systems, presenting an urgent need for rapid quantitative soil characterization. The current Swiss soil spectral library (SSL; n = 4374) in the mid-infrared range includes soil samples from the Biodiversity Monitoring Program (BDM), arranged in a regularly spaced grid across Switzerland, and temporally resolved data from the Swiss Soil Monitoring Network (NABO). Given that less than 2 % of the samples in the SSL originate from organic soils, we aimed to develop both an efficient calibration sampling scheme and accurate modeling strategy to estimate the soil carbon (SC) contents of heterogeneous samples between 0 and 2 m depth from 26 locations within two drained peatland regions (School of Agricultural, Forest and Food Sciences (HAFL) data set; n = 116). The focus was on minimizing the need for new reference analyses by efficiently mining the spectral information of the SSL. We used partial least square regressions (PLSRs), together with five repetitions of a location-grouped, 10-fold cross-validation, to predict SC ranging from 1 % to 52 % in the local HAFL data set. We compared the validation performance of different calibration schemes involving local models (1), models using the entire SSL combined with local samples (2), commonly referred to as spiking, and subsets of local and SSL samples optimized for the peatland target sites using the resampling local (RS-LOCAL) algorithm (3). Using local and RS-LOCAL calibrations with at least five local samples, we achieved similar validation results for predictions of SC up to 52 % (R2 = 0.93 to 0.97; bias = -0.07 to 1.65; root mean square error (RMSE) = 2.71 % to 3.89 % total carbon; ratio of performance to deviation (RPD) = 3.38 to 4.86; and ratio of performance to interquartile range (RPIQ) = 4.93 to 7.09). However, calibrations using RS-LOCAL only required five or 10 local samples for very accurate models (RMSE = 3.16 % and 2.71 % total carbon, respectively), while purely local calibrations required 50 samples for similarly accurate results (RMSE < 3 % total carbon). Of the three approaches, the entire SSL spiked with local samples for model calibration led to validations with the lowest performance in terms of R2, bias, RMSE, RPD and RPIQ. Hence, we show that a simple and comprehensible modeling approach, using RS-LOCAL together with a SSL, is an efficient and accurate strategy when using infrared spectroscopy. It decreases field and laboratory work, the bias of SSL spiking approaches and the uncertainty of local models. If adequately mined, the information in the SSL is sufficient to predict SC in new and independent study regions, even if the local soil characteristics are very different from the ones in the SSL. This will help to efficiently scale up the acquisition of quantitative soil information over space and time.

scholarly journals Predicting soil carbon by efficiently using variation in a mid-IR soil spectral library

2021 ◽  
Author(s):  
Anatol Helfenstein ◽  
Philipp Baumann ◽  
Raphael Viscarra Rossel ◽  
Andreas Gubler ◽  
Stefan Oechslin ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Scale Up ◽  
Monitoring Program ◽  
Partial Least Square ◽  
Least Square ◽  
Total Carbon ◽  
Local Algorithm ◽  
Organic Soils ◽  
Infrared Range ◽  
Spectral Library

Abstract. Traditional laboratory methods of acquiring soil information remain important for assessing key soil properties, soil functions and ecosystem services over space and time. Infrared spectroscopic modelling can link and massively scale up these methods for many soil characteristics in a cost-effective and timely manner. In Switzerland, only 10 % to 15 % of agricultural soils have been mapped sufficiently to serve spatial decision support systems, presenting an urgent need for rapid quantitative soil characterization. The current Swiss soil spectral library (SSL; n = 4374) in the mid-infrared range includes soil samples from the Biodiversity Monitoring Program (BDM), arranged in a regularly spaced grid across Switzerland, and temporally-resolved data from the Swiss Soil Monitoring Network (NABO). Given the relatively low representation of organic soils and their organo-mineral diversity in the SSL, we aimed to develop both an efficient calibration sampling scheme and accurate modelling strategy to estimate soil carbon (SC) contents of heterogeneous samples between 0 m to 2 m depth from 26 locations within two drained peatland regions (HAFL dataset; n = 116). The focus was on minimizing the need for new reference analyses by efficiently mining the spectral information of SSL instances and their target-feature representations. We used partial least square regressions (PLSR) together with a 5 times repeated, grouped by location, 10-fold cross validation (CV) to predict SC ranging from 1 % to 52 % in the local HAFL dataset. We compared the validation performance of different calibration schemes involving local models (1), models using the entire SSL spiked with local samples (2) and 15 subsets of local and SSL samples using the RS-LOCAL algorithm (3). Using local and RS-LOCAL calibrations with at least 5 local samples, we achieved similar validation results for predictions of SC up to 52 % (R2 = 0.94–0.96, bias = −0.6–1.5, RMSE = 2.6 % to 3.5 % total carbon). However, calibrations of representative SSL and local samples using RS-LOCAL only required 5 local samples for very accurate models (RMSE = 2.9 % total carbon), while local calibrations required 50 samples for similarly accurate results (RMSE 

scholarly journals Delineate Soil Characteristics and Carbon Pools in Grassland Compared to Native Forestland of India: A Meta-Analysis

Agronomy ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1969
Author(s):  
Rajeev Padbhushan ◽  
Sheetal Sharma ◽  
D. S. Rana ◽  
Upendra Kumar ◽  
Anshuman Kohli ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Microbial Biomass Carbon ◽  
Meta Analysis ◽  
Subsurface Layer ◽  
Management Strategies ◽  
Soil Characteristics ◽  
Total Carbon ◽  
Grass Species ◽  
Carbon Pools ◽  
Labile Carbon

Grassland is a highly dynamic land use system and it provides vital ecosystem services, mainly consisting of carbon storage in the tropics and subtropics. The objective of this study was to delineate grassland in India according to soil characteristics and carbon pools in comparison to native forestland, and to discuss management strategies for improving soil carbon (SC) storage in grassland. A total of 675 paired datasets from studies on grassland and forestland in India generated during the period of 1990–2019 were used for meta-analysis study. The analysis shows that soil pH and bulk density (BD) in grasslands were greater by 1.1% and 1.0% compared to forestlands while soil organic carbon (SOC) declined by 36.3% (p < 0.05). Among carbon pools, labile carbon (LC), non-labile carbon (NLC), and microbial biomass carbon (MBC) were 35.5%, 35.3% and 29.5% lower, respectively, in the grassland compared to the forestland. Total carbon (TC) was 35.0% lower in the grassland than the forestland (p < 0.05). Soil carbon stocks (SCS) were 32.8% lower in the grassland compared to the forestland. In the grassland, MBC/SOC (%) from the surface layer and subsurface layer were lower by 2.4% and 8.5%, respectively compared to forestland. The percentage effect size was found to have decreased from surface soil to subsurface soil. Relative SCS loss and carbon dioxide equivalent emission from the grassland compared to forestland were 15.2% and 33.3 Mg ha−1, respectively (p < 0.05). Proper management strategies like agroforestry, legume introduction, silvipastoral system, fertilization, irrigation, and quality grass species could improve SC storage and reduce SCS loss in grassland. Overall, this study gives an idea that conversion of native forestland into grassland in India has declined the SC content and hence it is necessary to adapt proper strategies to manage the soil-atmosphere carbon balance.

Accounting for space and time in soil carbon dynamics in timbered rangelands

2012 ◽  
Vol 38 (1) ◽  
pp. 51-64 ◽  
Author(s):  
Christopher Dean ◽  
Stephen H. Roxburgh ◽  
Richard J. Harper ◽  
David J. Eldridge ◽  
Ian W. Watson ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Carbon Dynamics ◽  
Soil Carbon Dynamics ◽  
Space And Time

scholarly journals Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

2015 ◽  
Vol 8 (2) ◽  
pp. 1787-1832 ◽  
Author(s):  
J. Heymann ◽  
M. Reuter ◽  
M. Hilker ◽  
M. Buchwitz ◽  
O. Schneising ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Detailed Comparison ◽  
Mean Difference ◽  
Total Carbon ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Time Period ◽  
Comparison Results ◽  
The Future

Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY onboard ENVISAT (March 2002–April 2012) and TANSO-FTS onboard GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System (IFS). We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between the SCIAMACHY and the GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT-SCIAMACHY (linear correlation coefficient r = 0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT-TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY-TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (±2 h, 10° × 10° around TCCON sites), i.e., the observed air masses are not exactly identical, but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by using also data from other missions (e.g., OCO-2, GOSAT-2, CarbonSat) in the future.

From managing customers to joint venturing with customers: co-creating service value in the digital age

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Christine Falkenreck ◽  
Ralf Wagner
Keyword(s):  
Perceived Risk ◽  
Structural Equation ◽  
Business Models ◽  
Joint Venture ◽  
Digital Age ◽  
Partial Least Square ◽  
Least Square ◽  
Equation Modeling ◽  
Data Set ◽  
Content Type

Purpose Until today, scholars claim that the phenomenon of “co-creation” of value in an “interacted” economy and in the context of positive actor-to-actor relationships has not been adequately explored. This study aims to first to identify and separate the accessible values of internet of things (IoT)-based business models for business-to-business (B2B) and business-to-government (B2G) customer groups. It quantifies the drivers to successfully implement disruptive business models. Design/methodology/approach Data were gathered from 292 customers in Western Europe. The conceptual framework was tested using partial least square structural equation modeling. Findings Managing disruptions in the digital age is closely related to the fact that the existing trust in buyer-seller relationships is not enough to accept IoT projects. A company’s digitalization capabilities, satisfaction with the existing relationship and trust in the IoT credibility of the manufacturer drives the perceived value of IoT-based business models in B2B settings. Contrastingly, in B2G settings, money is less important. Research limitations/implications Research refers to one business field, the data set is of European origin only. Findings indicate that the drivers to engage in IoT-related projects differ significantly between the customer groups and therefore require different marketing management strategies. Saving time today is more important to B2G buyers than saving money. Practical implications The disparate nature of B2B and B2G buyers indicates that market segmentation and targeted marketing must be considered before joint-venturing in IoT business models. To joint venture supply chain partners co-creating value in the context of IoT-related business models, relationship management should be focused with buyers on the same footing, as active players and co-developers of a personalized experience in digital service projects. Originality/value Diverging from established studies focusing on the relationship within a network of actors, this study defines disruptive business models and identifies its drivers in B2B and B2G relationships. This study proposes joint venturing with B2B and B2G customers to overcome the perceived risk of these IoT-related business models. Including customers in platforms and networks may lead to the co-creation of value in joint IoT projects.

scholarly journals N-Linked Glycopeptide Identification Based on Open Mass Spectral Library Search

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Zhiwu An ◽  
Qingbo Shu ◽  
Hao Lv ◽  
Lian Shu ◽  
Jifeng Wang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Human Cancer ◽  
Cancer Cell Line ◽  
Peptide Identification ◽  
High Energy ◽  
Mass Spectral Library ◽  
Spectral Library ◽  
Data Set ◽  
Library Search ◽  
Mass Spectral

Confident characterization of intact glycopeptides is a challenging task in mass spectrometry-based glycoproteomics due to microheterogeneity of glycosylation, complexity of glycans, and insufficient fragmentation of peptide bones. Open mass spectral library search is a promising computational approach to peptide identification, but its potential in the identification of glycopeptides has not been fully explored. Here we present pMatchGlyco, a new spectral library search tool for intact N-linked glycopeptide identification using high-energy collisional dissociation (HCD) tandem mass spectrometry (MS/MS) data. In pMatchGlyco, (1) MS/MS spectra of deglycopeptides are used to create spectral library, (2) MS/MS spectra of glycopeptides are matched to the spectra in library in an open (precursor tolerant) manner and the glycans are inferred, and (3) a false discovery rate is estimated for top-scored matches above a threshold. The efficiency and reliability of pMatchGlyco were demonstrated on a data set of mixture sample of six standard glycoproteins and a complex glycoprotein data set generated from human cancer cell line OVCAR3.

scholarly journals The use of radiocarbon <sup>14</sup>C to constrain carbon dynamics in the soil module of the land surface model ORCHIDEE (SVN r5165)

2018 ◽  
Author(s):  
Marwa Tifafi ◽  
Marta Camino-Serrano ◽  
Christine Hatté ◽  
Hector Morras ◽  
Lucas Moretti ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Carbon ◽  
Land Surface ◽  
Carbon Stock ◽  
Land Surface Model ◽  
Carbon Dynamics ◽  
Total Carbon ◽  
Soil Parameters ◽  
Surface Model

Abstract. Despite the importance of soil as a large component of the terrestrial ecosystems, the soil compartments are not well represented in the Land Surface Models (LSMs). Indeed, soils in current LSMs are generally represented based on a very simplified schema that can induce a misrepresentation of the deep dynamics of soil carbon. Here, we present a new version of the IPSL-Land Surface Model called ORCHIDEE-SOM, incorporating the 14C dynamic in the soil. ORCHIDEE-SOM, first, simulates soil carbon dynamics for different layers, down to 2 m depth. Second, concentration of dissolved organic carbon (DOC) and its transport are modeled. Finally, soil organic carbon (SOC) decomposition is considered taking into account the priming effect. After implementing the 14C in the soil module of the model, we evaluated model outputs against observations of soil organic carbon and 14C activity (F14C) for different sites with different characteristics. The model managed to reproduce the soil organic carbon stocks and the F14C along the vertical profiles. However, an overestimation of the total carbon stock was noted, but was mostly marked on the surface. Then, thanks to the introduction of 14C, it has been possible to highlight an underestimation of the age of carbon in the soil. Thereafter, two different tests on this new version have been established. The first was to increase carbon residence time of the passive pool and decrease the flux from the slow pool to the passive pool. The second was to establish an equation of diffusion, initially constant throughout the profile, making it vary exponentially as a function of depth. The first modifications did not improve the capacity of the model to reproduce observations whereas the second test showed a decrease of the soil carbon stock overestimation, especially at the surface and an improvement of the estimates of the carbon age. This assumes that we should focus more on vertical variation of soil parameters as a function of depth, mainly for diffusion, in order to upgrade the representation of global carbon cycle in LSMs, thereby helping to improve predictions of the future response of soil organic carbon to global warming.

scholarly journals Entrepreneurial self-efficacy and entrepreneurial intention: The mediating role of the need for independence

2021 ◽  
Vol 17 (4) ◽  
pp. 91-119
Author(s):  
Victor Osadolor ◽  
◽  
Kalu Emmanuel Agbaeze ◽  
Ejikeme Emmanuel Isichei ◽  
Samuel Taiwo Olabosinde ◽  
...  
Keyword(s):  
Self Efficacy ◽  
Structural Equation ◽  
Entrepreneurial Intention ◽  
Partial Least Square ◽  
Least Square ◽  
Data Set ◽  
Behavioral Reasoning Theory ◽  
The Relationship ◽  
Behavioral Reasoning

PURPOSE: The paper focuses on assessing the direct effect of entrepreneurial self-efficacy and entrepreneurial intention and the indirect effect of the need for independence on the relationship between the constructs. Despite increased efforts towards steering the interest of young graduates towards entrepreneurial venture, the response rate has been rather unimpressive and discouraging, thus demanding the need to account for what factors could drive intention towards venture ownership among graduates in Nigeria. METHODOLOGY: A quantitative approach was adopted and a data set from 235 graduates was used for the study. The data was analyzed using the partial least square structural equation model (PLS-SEM). FINDINGS: It was found that self-efficacy does not significantly affect intention. It was also found that the need for independence affects entrepreneurial intention. The study found that the need for independence fully mediates the relationship between entrepreneurial self-efficacy and entrepreneurial intention. PRACTICAL IMPLICATIONS: This paper provides new insight into the behavioral reasoning theory, through its application in explaining the cognitive role of the need for independence in decision-making, using samples from a developing economy. ORIGINALITY AND VALUE: The study advances a new perspective on the underlining factors that account for an entrepreneur’s intent to start a business venture, most especially among young graduates in Nigeria, through the lens of the behavioral reasoning theory. We further support the application of the theory in entrepreneurship literature, given the paucity of studies that have adopted the theory despite its relevance.

Sign in / Sign up

Export Citation Format

Share Document