A hierarchical Bayesian approach to dynamic ordinary differential equations modeling for repeated measures data on wheat growth

Poudel, Pratishtha; Bello, Nora M.; Lollato, Romulo P.; Alderman, Phillip D.

doi:10.1016/j.fcr.2022.108549

Cited by 8 publications

(8 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of course, when explicit solutions exist to the differential equation system, a statistical approach to our problem could be found in the application of non-linear mixed models ( Pinheiro and Bates, 2000 ). Without such explicit solutions, Bayesian hierarchical approaches may offer the best perspectives ( Poudel et al., 2022 ), which is the route we will explore in a follow-up paper.…”

Section: Discussionmentioning

confidence: 99%

“…Below we introduce four dynamic models, all containing some essential first principles of crop growth. The models we develop are extensions of general continuous model frameworks presented in the literature suitable for describing the growth of plants in an ecological context ( Paine et al., 2012 ) or the within-season accumulation of crop biomass ( Poudel et al., 2022 ). Though simple and largely phenomenological, the models are dynamic and therefore offer a biological interpretation of the parameters as well as the ability to produce varied output depending on environmental or management inputs.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations: a model library

et al. 2023

View full text Add to dashboard Cite

IntroductionDynamic crop growth models are an important tool to predict complex traits, like crop yield, for modern and future genotypes in their current and evolving environments, as those occurring under climate change. Phenotypic traits are the result of interactions between genetic, environmental, and management factors, and dynamic models are designed to generate the interactions producing phenotypic changes over the growing season. Crop phenotype data are becoming increasingly available at various levels of granularity, both spatially (landscape) and temporally (longitudinal, time-series) from proximal and remote sensing technologies.MethodsHere we propose four phenomenological process models of limited complexity based on differential equations for a coarse description of focal crop traits and environmental conditions during the growing season. Each of these models defines interactions between environmental drivers and crop growth (logistic growth, with implicit growth restriction, or explicit restriction by irradiance, temperature, or water availability) as a minimal set of constraints without resorting to strongly mechanistic interpretations of the parameters. Differences between individual genotypes are conceptualized as differences in crop growth parameter values.ResultsWe demonstrate the utility of such low-complexity models with few parameters by fitting them to longitudinal datasets from the simulation platform APSIM-Wheat involving in silico biomass development of 199 genotypes and data of environmental variables over the course of the growing season at four Australian locations over 31 years. While each of the four models fits well to particular combinations of genotype and trial, none of them provides the best fit across the full set of genotypes by trials because different environmental drivers will limit crop growth in different trials and genotypes in any specific trial will not necessarily experience the same environmental limitation.DiscussionA combination of low-complexity phenomenological models covering a small set of major limiting environmental factors may be a useful forecasting tool for crop growth under genotypic and environmental variation.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations: a model library

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Many time-series models have been proposed for analyzing crop phenology. Such models include shape-model fitting (Sakamoto et al, 2013;Zhou et al, 2020), random regression with the Legendre polynomial (Campbell et al, 2018;Campbell et al, 2019), segmented linear regression (Toda et al, 2021), and non-linear growth curves (Chang et al, 2017;Grados et al, 2020;Poudel et al, 2022). Anderson et al (2019) applied a three-parameter logistic model (S-shape non-linear curve) to maize CH time-series data measured by UAV over 1 year, applied a linear mixed effects (LME) model to the logistic parameters, decomposed the parameter variance into genetic and environmental effects: they showed that some of the parameters could be used as predictors of grain yield.…”

Section: Convergence and Future Prospectsmentioning

confidence: 99%

High-Throughput Field Phenotyping to Advance Precision Agriculture and Enhance Genetic Gain, Volume II

2024

Frontiers Research Topics

View full text Add to dashboard Cite

Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Many time-series models have been proposed for analyzing crop phenology. Such models include shape-model fitting ( Sakamoto et al., 2013 ; Zhou et al., 2020 ), random regression with the Legendre polynomial ( Campbell et al., 2018 ; Campbell et al., 2019 ), segmented linear regression ( Toda et al., 2021 ), and non-linear growth curves ( Chang et al., 2017 ; Grados et al., 2020 ; Poudel et al., 2022 ). Anderson et al.…”

Section: Introductionmentioning

confidence: 99%

Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice

et al. 2022

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) are powerful tools for monitoring crops for high-throughput phenotyping. Time-series aerial photography of fields can record the whole process of crop growth. Canopy height (CH), which is vertical plant growth, has been used as an indicator for the evaluation of lodging tolerance and the prediction of biomass and yield. However, there have been few attempts to use UAV-derived time-series CH data for field testing of crop lines. Here we provide a novel framework for trait prediction using CH data in rice. We generated UAV-based digital surface models of crops to extract CH data of 30 Japanese rice cultivars in 2019, 2020, and 2021. CH-related parameters were calculated in a non-linear time-series model as an S-shaped plant growth curve. The maximum saturation CH value was the most important predictor for culm length. The time point at the maximum CH contributed to the prediction of days to heading, and was able to predict stem and leaf weight and aboveground weight, possibly reflecting the association of biomass with duration of vegetative growth. These results indicate that the CH-related parameters acquired by UAV can be useful as predictors of traits typically measured by hand.

show abstract

A hierarchical Bayesian approach to dynamic ordinary differential equations modeling for repeated measures data on wheat growth

Cited by 8 publications

References 42 publications

A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations: a model library

A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations: a model library

High-Throughput Field Phenotyping to Advance Precision Agriculture and Enhance Genetic Gain, Volume II

Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice

Contact Info

Product

Resources

About