Estimation of the harvest index and the relative water content – Two examples of composite variables in agronomy

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Estimation of the harvest index and the relative water content – Two examples of composite variables in agronomy. / Jensen, Signe M.; Svensgaard, Jesper; Ritz, Christian.

In: European Journal of Agronomy, Vol. 112, 125962, 01.2020, p. 1-8.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Jensen, SM, Svensgaard, J & Ritz, C 2020, 'Estimation of the harvest index and the relative water content – Two examples of composite variables in agronomy', European Journal of Agronomy, vol. 112, 125962, pp. 1-8. https://doi.org/10.1016/j.eja.2019.125962

APA

Jensen, S. M., Svensgaard, J., & Ritz, C. (2020). Estimation of the harvest index and the relative water content – Two examples of composite variables in agronomy. European Journal of Agronomy, 112, 1-8. [125962]. https://doi.org/10.1016/j.eja.2019.125962

Vancouver

Jensen SM, Svensgaard J, Ritz C. Estimation of the harvest index and the relative water content – Two examples of composite variables in agronomy. European Journal of Agronomy. 2020 Jan;112:1-8. 125962. https://doi.org/10.1016/j.eja.2019.125962

Author

Jensen, Signe M. ; Svensgaard, Jesper ; Ritz, Christian. / Estimation of the harvest index and the relative water content – Two examples of composite variables in agronomy. In: European Journal of Agronomy. 2020 ; Vol. 112. pp. 1-8.

Bibtex

@article{2d346ce589ce4cabb7d5a7bae0b215f6,
title = "Estimation of the harvest index and the relative water content – Two examples of composite variables in agronomy",
abstract = "Composite variables are variables derived from measurable traits. They are commonly used in agronomy: two well-known examples being the harvest index and the relative water content. There are two approaches for finding estimated averages of such variables that are derived from direct measurements: They can be found either based on a calculation using individual measurements (“pre-processing”) or from a calculation using averages or estimates (“after-fitting”). The former needs to be done prior to fitting a statistical model whereas the latter is carried out after a statistical model has been fitted to the original measurements. We show that the commonly used pre-processing approach results in biased estimates. Moreover, the bias depends on both the correlation between and the uncertainty associated with the variables used for the composite variable. This finding is shown in two examples and a simulation study.",
keywords = "Agronomic indices, Estimating ratios, Marginal models, Nitrogen uptake",
author = "Jensen, {Signe M.} and Jesper Svensgaard and Christian Ritz",
year = "2020",
month = "1",
doi = "10.1016/j.eja.2019.125962",
language = "English",
volume = "112",
pages = "1--8",
journal = "European Journal of Agronomy",
issn = "1161-0301",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Estimation of the harvest index and the relative water content – Two examples of composite variables in agronomy

AU - Jensen, Signe M.

AU - Svensgaard, Jesper

AU - Ritz, Christian

PY - 2020/1

Y1 - 2020/1

N2 - Composite variables are variables derived from measurable traits. They are commonly used in agronomy: two well-known examples being the harvest index and the relative water content. There are two approaches for finding estimated averages of such variables that are derived from direct measurements: They can be found either based on a calculation using individual measurements (“pre-processing”) or from a calculation using averages or estimates (“after-fitting”). The former needs to be done prior to fitting a statistical model whereas the latter is carried out after a statistical model has been fitted to the original measurements. We show that the commonly used pre-processing approach results in biased estimates. Moreover, the bias depends on both the correlation between and the uncertainty associated with the variables used for the composite variable. This finding is shown in two examples and a simulation study.

AB - Composite variables are variables derived from measurable traits. They are commonly used in agronomy: two well-known examples being the harvest index and the relative water content. There are two approaches for finding estimated averages of such variables that are derived from direct measurements: They can be found either based on a calculation using individual measurements (“pre-processing”) or from a calculation using averages or estimates (“after-fitting”). The former needs to be done prior to fitting a statistical model whereas the latter is carried out after a statistical model has been fitted to the original measurements. We show that the commonly used pre-processing approach results in biased estimates. Moreover, the bias depends on both the correlation between and the uncertainty associated with the variables used for the composite variable. This finding is shown in two examples and a simulation study.

KW - Agronomic indices

KW - Estimating ratios

KW - Marginal models

KW - Nitrogen uptake

U2 - 10.1016/j.eja.2019.125962

DO - 10.1016/j.eja.2019.125962

M3 - Journal article

AN - SCOPUS:85072985700

VL - 112

SP - 1

EP - 8

JO - European Journal of Agronomy

JF - European Journal of Agronomy

SN - 1161-0301

M1 - 125962

ER -

ID: 234212682