Speaker
Description
1. Introduction
Although breeding progress is contributing to climate change mitigation through increased land use efficiency (Laidig et al., 2021), nitrogen use efficiency (Laidig et al., 2024), and a decreased carbon footprint (Riedesel et al., 2022), increasingly adverse weather conditions exert negative impacts on crop production and food security. Crop modeling serves as a powerful tool to investigate the interactions between genotype (G), environment (E), and management (M) by simulating the plant-soil-atmosphere system. Through in-silico experiments, crop models enable the simulation of crop growth, development, and yield formation under future climatic conditions. This study aims to define ideotypes, i.e., in-silico genotypes that exhibit high mean yields at high yield stability, and a minimized carbon footprint under different environments using the crop model CERES-Barley model embedded in DSSAT. By calibrating and evaluating the model based on field experiments and multi-environment trials, we aim to generate robust predictions and design a climate-smart in-silico genotype.
2. Material and Methods
To effectively employ crop simulation models, calibration for target genotypes and environments is essential, followed by performance evaluation to ensure accurate predictions. In this study, we use detailed multi-environment phenology, growth, and yield data of the elite barley cv. RGT Planet to parameterize the CERES-Barley model, based on an extensive two-year field experiment in Berlin Dahlem, where we collected growth and yield data under three irrigation treatments and a large dataset consisting of a multi-environment trial dataset covering 33 site years of pre-registration trials from 2014 to 2019. We use the time series estimator tool (TSE) for DSSAT to calibrate cultivar-specific coefficients by minimizing the normalized root mean square error (nRMSE) between simulated and observed data. We then use the parametrized and evaluated CERES-Barley model to investigate yield production under current and future climatic conditions. Through a hybrid method that combines the CERES-Barley crop model and life cycle assessment, we conduct in-silico experiments by simulating weather conditions based on the 17 RCP-climate scenarios of the DWD core ensemble (DWD, 2018) from 2006 to 2099. Subsequently, we cluster the yearly data into distinct groups according to environmental conditions (drought intensity, drought timing, extreme weather events, etc.) and their corresponding environmental impacts (i.e., carbon footprint). Using these weather clusters, we conduct sensitivity analysis to define various sets of model cultivar parameters, representing ideotypes with different combinations of genotypic traits aimed at achieving objectives such as high yields, yield stability, or low carbon footprint under various environmental conditions and management practices.
3. Results and discussion
The calibrated and evaluated CERES-Barley model provided robust simulation results for the elite cv. RGT Planet. The simulated yields show an increase in mid-century, under the RCP 4.5 and RCP 8.5, with a lower slope at the end of the century. The ideotyping exercise revealed significant variations in cultivar coefficients and corresponding crop traits across various environmental scenarios and management practices. This assessment facilitated the identification of ideotypes tailored for a maximized yield, yield stability, and a reduced carbon footprint. The results highlight the significance of integrating genotype × environment × management interactions in designing climate-smart ideotypes geared towards climate change mitigation while maximizing agricultural productivity.
4. References
Laidig, F., Feike, · T, Klocke, · B, Macholdt, · J, Miedaner, · T, Rentel, · D, & Piepho, · H P. (2021). Long-term breeding progress of yield, yield-related, and disease resistance traits in five cereal crops of German variety trials. Theoretical and Applied Genetics, 134, 3805–3827. https://doi.org/10.1007/s00122-021-03929-5
Laidig, F., Feike, T., Lichthardt, C., Schierholt, A., & Piepho, H. P. (2024). Breeding progress of nitrogen use efficiency of cereal crops, winter oilseed rape and peas in long-term variety trials. Theoretical and Applied Genetics, 137(2). https://doi.org/10.1007/S00122-023-04521-9
Riedesel, L., Laidig, F., Hadasch, S., Rentel, D., Hackauf, B., Piepho, H. P., & Feike, T. (2022). Breeding progress reduces carbon footprints of wheat and rye. Journal of Cleaner Production, 377, 134326. https://doi.org/10.1016/J.JCLEPRO.2022.134326
| Keywords | Crop model; DSSAT; CERES-Barley; spring barley; climate change mitigation |
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