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.
I: European Journal of Agronomy, Bind 112, 125962, 01.2020, s. 1-8.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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