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Catherine Roumet – One of the best experts on this subject based on the ideXlab platform.

  • Quantifying species composition in Root mixtures using two methods: near‐infrared reflectance spectroscopy and plant wax markers
    The New phytologist, 2006
    Co-Authors: Catherine Roumet, Catherine Picon-cochard, Lorna Dawson, Richard Joffre, Alain Blanchard, Robert W. Mayes, Mark J. Brewer
    Abstract:

    Summary • Understanding of plant interactions is greatly limited by our ability to identify and quantify Roots belonging to different species. We proposed and compared two methods for estimating the Root biomass proportion of each species in artificial mixtures: near-infrared reflectance spectroscopy (NIRS) and plant wax markers. •T wo sets of artificial Root mixtures composed of two or three herbaceous species were prepared. The proportion of Root material of each species in mixtures was estimated from NIRS spectral data (i) and the concentration patterns of n –alkanes (ii), n -alcohols (iii), and n –alkanes + n -alcohols combined (iv). For each data set, calibration equations were developed using multivariate statistical models. • The botanical composition of Root mixtures was predicted well for all the species considered. The accuracy varied slightly among methods: alkanes < alcohols = alkanes + alcohols < NIRS. Correlation coefficients between predicted and Actual Root proportions ranged from 0.89 to 0.99 for alkanes + alcohols predictions and from 0.97 to 0.99 for NIRS predictions. • These two methods provide promising potential for understanding allocation patterns and competitive interactions.

  • Quantifying species composition in Root mixtures using two methods: near-infrared reflectance spectroscopy and plant wax markers
    New Phytologist, 2006
    Co-Authors: Catherine Roumet, Catherine Picon-cochard, Lorna Dawson, Richard Joffre, Robert Mayes, Alain Blanchard, Mark Brewer
    Abstract:

    Understanding of plant interactions is greatly limited by our ability to identify and quantify Roots belonging to different species. We proposed and compared two methods for estimating the Root biomass proportion of each species in artificial mixtures: near-infrared reflectance spectroscopy (NIRS) and plant wax markers. Two sets of artificial Root mixtures composed of two or three herbaceous species were prepared. The proportion of Root material of each species in mixtures was estimated from NIRS spectral data (i) and the concentration patterns of n-alkanes (ii), n-alcohols (iii), and n-alkanes +n-alcohols combined (iv). For each data set, calibration equations were developed using multivariate statistical models. The botanical composition of Root mixtures was predicted well for all the species considered. The accuracy varied slightly among methods: alkanes < alcohols = alkanes + alcohols < NIRS. Correlation coefficients between predicted and Actual Root proportions ranged from 0.89 to 0.99 for alkanes + alcohols predictions and from 0.97 to 0.99 for NIRS predictions. These two methods provide promising potential for understanding allocation patterns and competitive interactions.

Stuart E. Madnick – One of the best experts on this subject based on the ideXlab platform.

  • Dynamic analysis of combat vehicle accidents
    System Dynamics Review, 2009
    Co-Authors: Nathan A. Minami, Stuart E. Madnick
    Abstract:

    To the chagrin of well-intentioned Army leaders, dozens of soldiers are killed each year as a result of combat vehicle accidents. The objective of this study is to look beyond the events and symptoms of accidents which normally indicate human error, and instead study the upper-level organizational processes and problems that constitute the Actual Root causes of accidents. After a short review of the literature we report on our development of a system dynamics model. We then discuss the results of several simulations; these suggest that high-level decisions that balance mission rate and operations tempo with troop availability, careful management of the work–rest cycle for deployed troops, and improvement of the processes for evaluating the lessons learned from accidents would lead to a reduction in Army combat vehicle accidents. Copyright © 2009 John Wiley & Sons, Ltd.

  • Protecting the Force: Reducing Combat Vehicle Accidents via Improved Organizational Processes
    , 2007
    Co-Authors: Nathan A. Minami, Stuart E. Madnick
    Abstract:

    Despite extraordinary efforts by leaders at all levels throughout the U.S. Army, dozens of soldiers are killed each year as a result of both combat and motor vehicle accidents. The objective of this study is to look beyond the events and symptoms of accidents which normally indicate human error, and instead study the upper-level organizational processes and problems that may constitute the Actual Root causes of accidents. Critical to this process is identifying critical variables, establishing causality between variables, and quantifying variables that lead to both resilience against accidents and propensities for accidents. After reviewing the available literature we report on our development of a System Dynamics model, which is an analytical model of the system that allows for extensive simulation. The results of these simulations suggest that high-level decisions that balance mission rate and operations tempo with troop availability, careful management of the work-rest cycle for deployed troops, and improvement of the processes for evaluating the lessons learned from accidents, will lead to a reduction in Army combat and motor vehicle accidents.

  • Reducing Combat Vehicle Accidents via Improved Organizational Processes
    SSRN Electronic Journal, 2007
    Co-Authors: Nathan A. Minami, Stuart E. Madnick
    Abstract:

    Despite extraordinary efforts by leaders at all levels throughout the U.S. Army, dozens of soldiers are killed each year as a result of both combat and motor vehicle accidents. The objective of this study is to look beyond the events and symptoms of accidents which normally indicate human error, and instead study the upper-level organizational processes and problems that may constitute the Actual Root causes of accidents. Critical to this process is identifying critical variables, establishing causality between variables, and quantifying variables that lead to both resilience against accidents and propensities for accidents. After reviewing the available literature we report on our development of a System Dynamics model, which is an analytical model of the system that allows for extensive simulation. The results of these simulations suggest that high-level decisions that balance mission rate and operations tempo with troop availability, careful management of the work-rest cycle for deployed troops, and improvement of the processes for evaluating the lessons learned from accidents, will lead to a reduction in Army combat and motor vehicle accidents.

Mark Brewer – One of the best experts on this subject based on the ideXlab platform.

  • Quantifying species composition in Root mixtures using two methods: near-infrared reflectance spectroscopy and plant wax markers
    New Phytologist, 2006
    Co-Authors: Catherine Roumet, Catherine Picon-cochard, Lorna Dawson, Richard Joffre, Robert Mayes, Alain Blanchard, Mark Brewer
    Abstract:

    Understanding of plant interactions is greatly limited by our ability to identify and quantify Roots belonging to different species. We proposed and compared two methods for estimating the Root biomass proportion of each species in artificial mixtures: near-infrared reflectance spectroscopy (NIRS) and plant wax markers. Two sets of artificial Root mixtures composed of two or three herbaceous species were prepared. The proportion of Root material of each species in mixtures was estimated from NIRS spectral data (i) and the concentration patterns of n-alkanes (ii), n-alcohols (iii), and n-alkanes +n-alcohols combined (iv). For each data set, calibration equations were developed using multivariate statistical models. The botanical composition of Root mixtures was predicted well for all the species considered. The accuracy varied slightly among methods: alkanes < alcohols = alkanes + alcohols < NIRS. Correlation coefficients between predicted and Actual Root proportions ranged from 0.89 to 0.99 for alkanes + alcohols predictions and from 0.97 to 0.99 for NIRS predictions. These two methods provide promising potential for understanding allocation patterns and competitive interactions.

Mark J. Brewer – One of the best experts on this subject based on the ideXlab platform.

  • Quantifying species composition in Root mixtures using two methods: near‐infrared reflectance spectroscopy and plant wax markers
    The New phytologist, 2006
    Co-Authors: Catherine Roumet, Catherine Picon-cochard, Lorna Dawson, Richard Joffre, Alain Blanchard, Robert W. Mayes, Mark J. Brewer
    Abstract:

    Summary • Understanding of plant interactions is greatly limited by our ability to identify and quantify Roots belonging to different species. We proposed and compared two methods for estimating the Root biomass proportion of each species in artificial mixtures: near-infrared reflectance spectroscopy (NIRS) and plant wax markers. •T wo sets of artificial Root mixtures composed of two or three herbaceous species were prepared. The proportion of Root material of each species in mixtures was estimated from NIRS spectral data (i) and the concentration patterns of n -alkanes (ii), n -alcohols (iii), and n -alkanes + n -alcohols combined (iv). For each data set, calibration equations were developed using multivariate statistical models. • The botanical composition of Root mixtures was predicted well for all the species considered. The accuracy varied slightly among methods: alkanes < alcohols = alkanes + alcohols < NIRS. Correlation coefficients between predicted and Actual Root proportions ranged from 0.89 to 0.99 for alkanes + alcohols predictions and from 0.97 to 0.99 for NIRS predictions. • These two methods provide promising potential for understanding allocation patterns and competitive interactions.

Lorna Dawson – One of the best experts on this subject based on the ideXlab platform.

  • Quantifying species composition in Root mixtures using two methods: near‐infrared reflectance spectroscopy and plant wax markers
    The New phytologist, 2006
    Co-Authors: Catherine Roumet, Catherine Picon-cochard, Lorna Dawson, Richard Joffre, Alain Blanchard, Robert W. Mayes, Mark J. Brewer
    Abstract:

    Summary • Understanding of plant interactions is greatly limited by our ability to identify and quantify Roots belonging to different species. We proposed and compared two methods for estimating the Root biomass proportion of each species in artificial mixtures: near-infrared reflectance spectroscopy (NIRS) and plant wax markers. •T wo sets of artificial Root mixtures composed of two or three herbaceous species were prepared. The proportion of Root material of each species in mixtures was estimated from NIRS spectral data (i) and the concentration patterns of n -alkanes (ii), n -alcohols (iii), and n -alkanes + n -alcohols combined (iv). For each data set, calibration equations were developed using multivariate statistical models. • The botanical composition of Root mixtures was predicted well for all the species considered. The accuracy varied slightly among methods: alkanes < alcohols = alkanes + alcohols < NIRS. Correlation coefficients between predicted and Actual Root proportions ranged from 0.89 to 0.99 for alkanes + alcohols predictions and from 0.97 to 0.99 for NIRS predictions. • These two methods provide promising potential for understanding allocation patterns and competitive interactions.

  • Quantifying species composition in Root mixtures using two methods: near-infrared reflectance spectroscopy and plant wax markers
    New Phytologist, 2006
    Co-Authors: Catherine Roumet, Catherine Picon-cochard, Lorna Dawson, Richard Joffre, Robert Mayes, Alain Blanchard, Mark Brewer
    Abstract:

    Understanding of plant interactions is greatly limited by our ability to identify and quantify Roots belonging to different species. We proposed and compared two methods for estimating the Root biomass proportion of each species in artificial mixtures: near-infrared reflectance spectroscopy (NIRS) and plant wax markers. Two sets of artificial Root mixtures composed of two or three herbaceous species were prepared. The proportion of Root material of each species in mixtures was estimated from NIRS spectral data (i) and the concentration patterns of n-alkanes (ii), n-alcohols (iii), and n-alkanes +n-alcohols combined (iv). For each data set, calibration equations were developed using multivariate statistical models. The botanical composition of Root mixtures was predicted well for all the species considered. The accuracy varied slightly among methods: alkanes < alcohols = alkanes + alcohols < NIRS. Correlation coefficients between predicted and Actual Root proportions ranged from 0.89 to 0.99 for alkanes + alcohols predictions and from 0.97 to 0.99 for NIRS predictions. These two methods provide promising potential for understanding allocation patterns and competitive interactions.