Bayesian multi-level calibration of a process-based maize phenology model

Viswanathan, Michelle; Scheidegger, Andreas; Streck, Thilo; Gayler, Sebastian; Weber, Tobias

doi:10.1016/j.ecolmodel.2022.110154

Cited by 7 publications

(1 citation statement)

References 59 publications

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“…Wallach et al (2018) could demonstrate that it is feasible to include a cultivar-specific temperature response parameter ( T opt ) for flowering time predictions in common bean. Viswanathan et al (2022) were able to optimize two temperature response parameters ( T min and T opt ) for two growth stages in maize. For wheat, Rezaei et al (2018) found indications that cultivar-specific thermal requirements play a major role in the adaptation of wheat phenology.…”

Section: Introductionmentioning

confidence: 99%

From neglecting to including cultivar-specificper setemperature responses: Extending the concept of thermal time for plant development modeling

Roth,

Binder,

Kirchgessner

et al. 2023

Preprint

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SummaryPredicting plant development is a long-desired goal of crop physiology. Plant growth divides into two main, interwoven components: continuous growth, and the succession of growth stages (phenology). Growth is commonly modeled on species-level asper seresponse to temperature that is scaled to cultivar-specific intrinsic growth rates. Differences in phenology—exhibited by cultivars in different environments—are interpreted as interactions with remaining environmental covariates. We question this fundamental assumption and suspect that cultivar-specific temperature responses largely influence phenology.To gather evidence, temporally resolved plant organ tracking and field phenotyping data were collected over multiple years in winter wheat and soybean. The response of traits to temperature was examined using models of increasing complexity (ranging from linear models to hierarchical splines and neural networks). Additionally, the trait level (leaf growth, canopy development, stem elongation), the covariate level (soil temperature, air temperature) and the covariate measurement level (below/inside canopies, at a reference weather station) were varied.The cultivar-specific non-linear models explained, in contrast to thermal time, large proportions of phenology-related cultivar-by-environment interactions.We conclude that such unbiased predictions are essential in breeding and for the cultivation of crops in view of increasing heat, drought, and other adverse climatic conditions.

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Section: Introductionmentioning

confidence: 99%