Biology subjects

Seidel, S. J.

Publications and source records attributed to Seidel, S. J..

3 recordsLinked to original sources

bioRxiv2025

Winter rye root growth and plasticity in response to nitrogen and phosphorus omission under field conditions.

Nitrogen (N) and phosphorus (P) deficiencies can significantly reduce crop yield. Despite their importance, the impacts of N and P deficiencies under field conditions on cereal roots, particularly winter rye, remain poorly understood. This study investigates the effects of N and P deficiencies on winter rye growth and root architecture under field conditions. A sampling campaign was conducted during the 2022 season at the long-term fertilizer experiment Dikopshof, Germany. Four fertilizer treatments were chosen: (1) fully fertilized with manure (NPKCa+m+s), (2) fully fertilized without manure (NPKCa), (3) N omitted (_PKCa), and (4) P omitted (N_KCa). Shoot biomass was assessed at five growth stages, alongside with topsoil root biomass, number of nodal roots and tillers, and root angle. The results showed that shoot and root biomass were highest in the NPKCa+m+s treatment and lowest under N omission. Although the treatment ranking of root traits varied between dates, a trend for an enhanced number of roots in the N and P omission treatments was observed around flowering. P omission fostered an increased number of tillers and N omission caused steeper root angles compared to other treatments. These findings demonstrate the strong impact of the environment and development stage on root phenotypic plasticity.

physiology↗

bioRxiv2023

A calibration protocol for soil-crop models aimed at reducing prediction error and inter-model variability

Process-based soil-crop models are widely used in agronomic research. They are major tools for evaluating climate change impact on crop production. Multi-model simulation studies show a wide diversity of results among models, implying that simulation results are very uncertain. A major path to improving simulation results is to propose improved calibration practices that are widely applicable. This study proposes an innovative generic calibration protocol. The two major innovations concern the treatment of multiple output variables and the choice of parameters to estimate, both of which are based on standard statistical procedure adapted to the particularities of soil-crop models. The protocol performed well in a challenging artificial-data test. The protocol is formulated so as to be applicable to a wide range of models and data sets. If widely adopted, it could substantially reduce model error and inter-model variability, and thus increase confidence in soil-crop model simulations.

plant biology↗

bioRxiv2019

How well do crop models predict phenology, with emphasis on the effect of calibration?

Predicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population.

plant biology↗