% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@PHDTHESIS{Brogi:888221,
author = {Brogi, Cosimo},
title = {{G}eophysics-based soil mapping for improved modelling of
spatial variability in crop growth and yield},
volume = {518},
school = {Universität Stuttgart},
type = {Dissertation},
address = {Jülich},
publisher = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
reportid = {FZJ-2020-04774},
isbn = {978-3-95806-510-9},
series = {Schriften des Forschungszentrums Jülich. Reihe Energie
$\&$ Umwelt / Energy $\&$ Environment},
pages = {xxi, 127 S.},
year = {2020},
note = {Universität Stuttgart, Diss., 2019},
abstract = {Water shortage is one of the predominant factors that can
directly or indirectly cause a reduction in crop yield and
thus poses a severe threat to sustainable crop production.
It is therefore critical to improve the sustainability of
current agricultural management practices and develop new
strategies that will allow the establishment of more
sustainable agricultural production systems that can meet
present and future food demand. The use of agro-ecosystem
models to simulate crop growth for given environmental
conditions, and the use of detailed information on soil
heterogeneity beyond the field scale are among the most
promising tools for achieving this goal. Soil properties are
a key control for water and nutrient availability and are
therefore co-responsible for yield gaps and harvest
failures. A detailed representation of the spatial
variability of soil is consequently essential for
establishing relevant spatially distributed agro-ecosystem
simulations of crop performance in response to water stress.
Unfortunately, a detailed soil representation is costly to
obtain, and generally cannot be substituted by the use of
existing general-purpose soil maps that lack the necessary
level of detail. Recently, improvements in digital soil
mapping have been made using non-invasive geophysical
methods such as electromagnetic induction (EMI) that provide
fast and costeffective mapping of relevant soil information.
It is however still challenging to derive information
relevant for agricultural management from large geophysical
datasets and their added value for agricultural applications
has not been fully investigated yet, especially for the
analysis of patterns in crop performance. This thesis aims
at investigating and quantifying the added value of detailed
soil information obtained using large-scale geophysical
mapping for the simulation and prediction of the spatial
variability of crop growth and yield obtained with
agro-ecosystem modelling. [...]},
cin = {IBG-3},
cid = {I:(DE-Juel1)IBG-3-20101118},
pnm = {255 - Terrestrial Systems: From Observation to Prediction
(POF3-255)},
pid = {G:(DE-HGF)POF3-255},
typ = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
url = {https://juser.fz-juelich.de/record/888221},
}