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\begin{document}

\title{Data Source Prioritization Flowchart for Process-based Plant Model
Parametrization}



\author[1]{Luis Vargas Rojas}%
\author[1]{Sajad Jamshidi}%
\author[1]{Diane Wang}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\begin{abstract}
Dynamic process-based plant models are computerized representations of
plant growth, development, and productivity that use measurements of
environmental and physiological processes as input data to make
predictions. However, the use of process-based models in phenotyping
programs still faces challenge to parameterization across multiple
genotypes, such as (1) the need for extensive and intensive datasets as
parameters for running simulations, many of which are obtained using
destructive and disruptive sampling, (2) the lack of systematic
approaches needed to parameterize across extensive collections of
genetically distinct, but often related, individuals that are typical of
breeding populations. Remote and proximal sensing are potential
alternative sources of data to inform parameters of process-based plant
models because they can provide fast and non-destructive estimations of
plant biophysical parameters across spatial and temporal scales. Over
the years, several approaches have been proposed to leverage sensing for
model parameterization, from simple empirical tuning to inverse modeling
approaches. Continued inquiry into how best to use remote and proximal
sensing data to estimate model parameters is critical to the future
scale-out of these models for breeding programs and simulating processes
that underlie complex genotype-by-environment interactions. Here, we
present a decision flowchart to provide a visual representation of the
sources and steps for process-based model parameterization that could be
used as a guide for researchers working with remote sensing data and
crop modeling across numerous genotypes.%
\end{abstract}%



\sloppy


\emph{\textbf{Luis Vargas-Rojas\textsuperscript{\emph{1}}}, Sajad
Jamshidi\textsuperscript{1,} Diane Wang\textsuperscript{1}}

\emph{\textsuperscript{1}Purdue University, West Lafayette, United
States}

\textbf{ORCiD:} {[}0000-0001-8610-9901{]}

\textbf{Keywords:} Remote and proximal sensing, crop modelling, decision
flowchart, parameterization, estimations, integration, scale-out.

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