scholarly journals Estimating maximum fine-fraction organic carbon in UK grasslands

2021 ◽  
Vol 18 (2) ◽  
pp. 605-620
Author(s):  
Kirsty C. Paterson ◽  
Joanna M. Cloy ◽  
Robert M. Rees ◽  
Elizabeth M. Baggs ◽  
Hugh Martineau ◽  
...  
Keyword(s):  
Linear Regression ◽  
Organic Carbon ◽  
Quantile Regression ◽  
Fine Fraction ◽  
Boundary Line ◽  
Regression Equations ◽  
Grassland Soils ◽  
Soil Fraction ◽  
Fine Soil ◽  
Sequestration Potential

Abstract. Soil organic carbon (SOC) sequestration across agroecosystems worldwide can contribute to mitigate the effects of climate change by reducing levels of atmospheric CO2. Stabilisation of organic carbon (OC) in the fine soil fraction (< 20 µm) is considered an important long-term store of SOC, and the saturation deficit (difference between measured OC and estimated maximum OC in the fine fraction) is frequently used to assess SOC sequestration potential following the linear regression equation developed by Hassink (1997). However, this approach is often taken without any assessment of the fit of the equation to the soils being studied. The statistical limitations of linear regression have previously been noted, giving rise to the proposed use of boundary line (BL) analysis and quantile regression (QR) to provide more robust estimates of maximum SOC stabilisation. The objectives of this work were to assess the suitability of the Hassink (1997) equation to estimate maximum fine-fraction OC in UK grassland soils of varying sward ages and to evaluate the linear regression, boundary line and quantile regression methods to estimate maximum fine-fraction OC. A chronosequence of 10 grasslands was sampled, in order to assess the relationship between sward age (time since the last reseeding event) and the measured and predicted maximum fine-fraction OC. Significantly different regression equations show that the Hassink (1997) equation does not accurately reflect maximum fine-fraction OC in UK grasslands when determined using the proportion of the fine soil fraction (< 20 µm, %) and measured fine-fraction OC (g C per kg soil). The QR estimate of maximum SOC stabilisation was almost double that of the linear regression and BL analysis (0.89 ± 0.074, 0.43 ± 0.017 and 0.57 ± 0.052 g C per kg soil, respectively). Sward age had an inconsistent effect on the measured variables and potential maximum fine-fraction OC. Fine-fraction OC across the grasslands made up 4.5 % to 55.9 % of total SOC, implying that there may be either high potential for additional C sequestration in the fine fraction of these soils or that protection in aggregates is predominant in these grassland soils. This work highlights the need to ensure that methods used to predict maximum fine-fraction OC reflect the soil in situ, resulting in more accurate assessments of carbon sequestration potential.

scholarly journals Estimating maximum mineral associated organic carbon in UK grasslands

2020 ◽  
Author(s):  
Kirsty C. Paterson ◽  
Joanna M. Cloy ◽  
Robert M. Rees ◽  
Elizabeth M. Baggs ◽  
Hugh Martineau ◽  
...  
Keyword(s):  
Linear Regression ◽  
Organic Carbon ◽  
C Sequestration ◽  
Boundary Line ◽  
Regression Equations ◽  
Grassland Soils ◽  
Carbon Sequestration Potential ◽  
Soil Fraction ◽  
Fine Soil ◽  
Sequestration Potential

Abstract. Soil organic carbon (SOC) sequestration across agroecosystems worldwide can contribute to mitigate the effects of climate change by reducing levels of atmospheric CO2. Mineral associated organic carbon (MAOC) is considered an important long-term store of SOC and the saturation deficit (difference between measured MAOC and estimated maximum MAOC) is frequently used to assess SOC sequestration potential following the linear regression equation developed by Hassink (1997). However, this approach is often taken without any assessment of the fit of the equation to the soils being studied. The statistical limitations of linear regression have previously been noted, giving rise to the proposed use of boundary line (BL) analysis and quantile regression (QR) to provide more robust estimates of maximum SOC stabilisation. The objectives of this work were to assess the suitability of the Hassink (1997) equation to estimate maximum MAOC in UK grassland soils of varying sward ages and to evaluate the linear regression, BL and QR methods to estimate maximum MAOC. A chronosequence of 10 grasslands was sampled, in order to assess the relationship between sward age (time since last reseeding event) and current and predicted maximum MAOC. Significantly different regression equations show that the Hassink (1997) equation does not accurately reflect maximum MAOC in UK grasslands when determined using the proportion of fine soil fraction and current MAOC. The QR estimate of maximum SOC stabilisation was almost double that of linear regression and BL analysis (0.89 ± 0.074, 0.43 ± 0.017 and 0.57 ± 0.052 g C kg−1 soil, respectively). Sward age had an inconsistent effect on the measured variables and potential maximum MAOC. MAOC across the grasslands made up 4.5 to 55.9 % of total SOC, implying that there may be either high potential for additional C sequestration in the mineral fraction of these soils, or stabilisation in aggregates is predominant in these grassland soils. This work highlights the need to ensure that methods used to predict maximum MAOC reflect the soil in situ, resulting in more accurate assessments of carbon sequestration potential.

Soil organic carbon dynamics and sequestration potential in cropland-grassland agro-ecosystems

2021 ◽  
Author(s):  
Thomas Guillaume ◽  
David Makowski ◽  
Zamir Libohova ◽  
Luca Bragazza ◽  
Sokrat Sinaj
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Management Practices ◽  
Storage Capacity ◽  
Agricultural Soils ◽  
C Sequestration ◽  
Boundary Line ◽  
Monitoring Networks ◽  
Soil C ◽  
Sequestration Potential

&lt;p&gt;Increasing soil organic carbon (SOC) in agro-ecosystems enables to address simultaneously food security as well as climate change adaptation and mitigation. Croplands represent a great potential to sequester atmospheric C because they are depleted in SOC. Hence, reliable estimations of SOC deficits in agro-ecosystems are crucial to evaluate the C sequestration potential of agricultural soils and support management practices. Using a 30-year old soil monitoring networks with 250 sites established in western Switzerland, we identified factors driving the long-term SOC dynamics in croplands (CR) and permanent grasslands (PG) and quantified SOC deficit. A new relationship between the silt + clay (SC) soil particles and the C stored in the mineral-associated fraction (MAOMC) was established. We also tested the assumption about whether or not PG can be used as carbon-saturated reference sites. The C-deficit in CR constituted about a third of their potential SOC content and was mainly affected by the proportion of temporary grassland in the crop rotation. SOC accrual or loss were the highest in sites that experienced land-use change. The MAOMC level in PG depended on the C accrual history, indicating that C-saturation level was not coincidental. Accordingly, the relationship between MAOMC and SC to determine soil C-saturation should be estimated by boundary line analysis instead of least squares regressions. In conclusion, PG do provide an additional SOC storage capacity under optimal management, though the storage capacity is greater for CR.&lt;/p&gt;

SINGLE-FACTOR LINEAR REGRESSION MODEL OF HODGKIN’S LYMPHOMA IN ADOLESCENTS

2017 ◽  
Vol 63 (2) ◽  
pp. 320-325
Author(s):  
Svetlana Kuleva ◽  
Svetlana Ivanova ◽  
Andrey Karitskiy ◽  
Darya Zvyagintseva
Keyword(s):  
Radiation Therapy ◽  
Linear Regression ◽  
Predictive Factors ◽  
Hodgkin’S Lymphoma ◽  
Risk Group ◽  
Hodgkin's Lymphoma ◽  
Regression Equations ◽  
Single Factor ◽  
Number Of Cycles ◽  
Selection Of

With the use of regression and dispersion analyzes conducted in this study it was possible to identify predictive factors that significantly worsen the prognosis of Hodgkin’s lymphoma in adolescents however constructed regression equations did not pass the quality assessment of the models, which did not allow them to be used for the construction of point and interval predictions. In a cohort of patients enrolled in the study a risk-adapted treatment program based on the risk group for stratification was conducted. Negative aspects of the results of our study could be related to the already selected volume of the program for these patients. In turn our analysis proves the validity of the selection of therapeutic loads (a number of cycles of polychemotherapy, doses of radiation therapy) depending on the risk group, into which the adolescent is stratified.

Soil organic carbon storage by shaded and unshaded coffee systems and its implications for climate change mitigation in China

2021 ◽  
pp. 1-8
Author(s):  
Ziwei Xiao ◽  
Xuehui Bai ◽  
Mingzhu Zhao ◽  
Kai Luo ◽  
Hua Zhou ◽  
...  
Keyword(s):  
Climate Change ◽  
Linear Regression ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Multiple Linear Regression ◽  
Carbon Storage ◽  
Soil Ph ◽  
Soil Bulk Density ◽  
Soil N ◽  
And Storage

Abstract Shaded coffee systems can mitigate climate change by fixation of atmospheric carbon dioxide (CO2) in soil. Understanding soil organic carbon (SOC) storage and the factors influencing SOC in coffee plantations are necessary for the development of sound land management practices to prevent land degradation and minimize SOC losses. This study was conducted in the main coffee-growing regions of Yunnan; SOC concentrations and storage of shaded and unshaded coffee systems were assessed in the top 40 cm of soil. Relationships between SOC concentration and factors affecting SOC were analysed using multiple linear regression based on the forward and backward stepwise regression method. Factors analysed were soil bulk density (ρb), soil pH, total nitrogen of soil (N), mean annual temperature (MAT), mean annual moisture (MAM), mean annual precipitation (MAP) and elevations (E). Akaike's information criterion (AIC), coefficient of determination (R2), root mean square error (RMSE) and residual sum of squares (RSS) were used to describe the accuracy of multiple linear regression models. Results showed that mean SOC concentration and storage decreased significantly with depth under unshaded coffee systems. Mean SOC concentration and storage were higher in shaded than unshaded coffee systems at 20–40 cm depth. The correlations between SOC concentration and ρb, pH and N were significant. Evidence from the multiple linear regression model showed that soil bulk density (ρb), soil pH, total nitrogen of soil (N) and climatic variables had the greatest impact on soil carbon storage in the coffee system.

Methodology of Estimation of Fire Separation Distances between Construction Facilities by Calculation

2020 ◽  
Vol 1006 ◽  
pp. 93-100
Author(s):  
Vadym Nizhnyk ◽  
Yurii Feshchuk ◽  
Volodymyr Borovykov
Keyword(s):  
Mathematical Model ◽  
Heat Flux ◽  
Linear Regression ◽  
Ignition Temperature ◽  
Oil Products ◽  
Regression Equations ◽  
Fire Load ◽  
Literary Sources ◽  
Tabular Method

Based on analysis of appropriate literary sources we established that estimation of fire separation distances was based of two criteria: heat flux and temperature. We proposed to use “ignition temperature of materials” as principal criterion when determining fire separation distances between adjacent construction facilities. Based on the results derived while performing complete factorial we created mathematical model to describe trend of changing fire separation distances depending on caloric power of fire load (Q), openings factor of the external enclosing structures (k) and duration of irradiation (t); moreover, its adequacy was confirmed. Based on linear regression equations we substantiated calculation and tabular method for the determination of fire separation distances for a facility being irradiated which contains combustible or otherwise non-combustible façade and a facility where liquid oil products turn. We developed and proposed general methodology for estimation of fire separation distances between construction facilities by calculation.

Potential soil carbon sequestration of agricultural land around the world

2021 ◽  
Author(s):  
Sylvia Vetter ◽  
Michael Martin ◽  
Pete Smith
Keyword(s):  
Organic Carbon ◽  
Management Practices ◽  
Agricultural Land ◽  
Ghg Emissions ◽  
C Sequestration ◽  
Organic Soils ◽  
Soil C ◽  
Sequestration Potential ◽  
The World ◽  
Soil C Sequestration

&lt;p&gt;Reducing greenhouse gas (GHG) emissions in to the atmosphere to limit global warming is the big challenge of the coming decades. The focus lies on negative emission technologies to remove GHGs from the atmosphere from different sectors. Agriculture produces around a quarter of all the anthropogenic GHGs globally (including land use change and afforestation). Reducing these net emissions can be achieved through techniques that increase the soil organic carbon (SOC) stocks. These techniques include improved management practices in agriculture and grassland systems, which increase the organic carbon (C) input or reduce soil disturbances. The C sequestration potential differs among soils depending on climate, soil properties and management, with the highest potential for poor soils (SOC stock farthest from saturation).&lt;/p&gt;&lt;p&gt;Modelling can be used to estimate the technical potential to sequester C of agricultural land under different mitigation practices for the next decades under different climate scenarios. The ECOSSE model was developed to simulate soil C dynamics and GHG emissions in mineral and organic soils. A spatial version of the model (GlobalECOSSE) was adapted to simulate agricultural soils around the world to calculate the SOC change under changing management and climate.&lt;/p&gt;&lt;p&gt;Practices like different tillage management, crop rotations and residue incorporation showed regional differences and the importance of adapting mitigation practices under an increased changing climate. A fast adoption of practices that increase SOC has its own challenges, as the potential to sequester C is high until the soil reached a new C equilibrium. Therefore, the potential to use soil C sequestration to reduce overall GHG emissions is limited. The results showed a high potential to sequester C until 2050 but much lower rates in the second half of the century, highlighting the importance of using soil C sequestration in the coming decades to reach net zero by 2050.&lt;/p&gt;

scholarly journals PENGARUH TINGKAT PENDIDIKAN, MOTIVASI, DAN KOMPENSASI TERHADAP KINERJA PEGAWAI DI BRI CABANG SEMARANG AHMAD YANI

2019 ◽  
Vol 11 (02) ◽  
pp. 31-47
Author(s):  
Sopi Sopi ◽  
Zumrotun Nafi'ah
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Industrial Revolution ◽  
Employee Performance ◽  
Science And Technology ◽  
Regression Equations ◽  
Linear Regression Equation ◽  
Improve Performance ◽  
Independent Variables ◽  
Life Education

Education, motivation and compensation are important things that can improve performance. This study aims to explain whether there is an influence of education, motivation and compensation on employee performance. So that through the results of this study it is expected to be a reference for leaders in managing the organization. In this study there are three independent variables namely education, motivation and compensation and one dependent variable is employee performance. At present it is in the era of industrial revolution 4.0, which is marked by; big data / giant data, internet of think, labor knowledge, and long life education. Since the beginning of the life of mankind to an infinite period, it is largely determined by the mastery of science and technology. Science and technology can not be separated from the progress of education level. Education is the base of all changes both individually, as well as countries. Employee performance is determined by the education that is owned, as high as education, the higher the performance and vice versa. The population in this study are BRI CAB employees, SEMARANG A-YANI, 60 people and all of them are sampled. The results of the analysis using SPSS 23 program statistical tools obtained multiple linear regression equation Y = 0.505 X1 + 0.175 X2 + 0.408 X3 The results of multiple linear regression equations show that there is a positive and significant influence between education on employee performance at BRI CAB. A YANI SEMARANG (t count test 6.314> t table 0.05), motivation towards employee performance at BRI CAB. A YANI SEMARANG (tcount 2,160> t table 0,05), and compensation for employee performance at BRI CAB. A YANI SEMARANG (t test 5.108> ttable 0.05). While together (simultaneously) the influence of education, motivation and compensation has an effect on and significant on the performance of employees at BRI CAB. A YANI SEMARANG (count = 44,692> ftabel = 0.05). The influence of the two research variables is very strong with a correlation value of 69.0% for employee performance at BRI CAB. A YANI SEMARANG is influenced by the motivation and compensation education of the remaining 31.0% of the employees' performance at BRI CAB. A YANI SEMARANG is influenced by other variables that affect employee performance.

scholarly journals PENGARUH LINGKUNGAN KERJA DAN KOMPENSASI TERHADAP KINERJA PEGAWAI PADA DINAS PENDIDIKAN, PEMUDA DAN OLAHRAGA KABUPATEN MAJENE

2020 ◽  
Vol 9 (2) ◽  
pp. 121
Author(s):  
Sri Indira Hartawati ◽  
Meutia A Sahur
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Work Environment ◽  
Employee Performance ◽  
Linear Regression Analysis ◽  
Youth Sports ◽  
Multiple Linear Regression Analysis ◽  
Department Of Education ◽  
Test Reliability ◽  
Regression Equations

<p><em>This research was conducted at the Department of Education, Youth and Sports of Majene Regency with the title The Effect of Work Environment and Compensation on Employee Performance. The formulation of the problem used by researchers is How the influence of the Work Environment on Employee Performance at the Education and Youth Sports Office of Majene Regency, How the influence of Compensation on Employee Performance at the Education and Youth Sports Office of Majene Regency, which variables have more influence on Employee Performance at the Education and Youth Office Majene District Sports. The research method, namely the population and sample used in this study were all employees of the Department of Education and Youth Sports of Majene Regency, which amounted to about 50 people, while the analysis method used the Validity Test, Reliability Test, Multiple Linear Regression Analysis This analysis was used to determine how much influence it had. independent variables, namely: compensation (X1), and work environment (X2) on the dependent variable, namely Employee Performance (Y). Multiple linear regression equations, Partial Significance Test (t test) and Simultaneous Test (F test). The results obtained from this study are the work environment has a significant effect on employee performance at the Department of Education and Youth Sports of Majene Regency, compensation has an effect on employee performance at the Education and Youth Sports Office of Majene Regency. and the work environment has a more dominant influence on employee performance at the Department of Education and Youth Sport, Majene Regency.</em></p><p><strong><em>Keywords: </em></strong><em>Work Environment, Compensation, Employee Performance</em></p>

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