Speaker
Description
1. Introduction
Among climate change’s features, the recurrence of spring heat waves has become a growing threat to winter crops that complete their reproductive phase over the spring and early summer period (IPCC, 2021) such as winter oilseed rape (WOSR). Modelling approaches to predict and to help anticipating risks of heat stress over crop sensitive stages (e.g. using early or late flowering varieties) could be reliable decision-making tools. The SuMoToRI crop model was previously designed to predict the effects of sulfur (S) availability in WOSR which is a high S-demanding crop (Brunel-Muguet et al. 2015, Poisson et al. 2018). Recent developments have focused on testing the robustness of the model production under repeated heat stresses. Experimental results have shown that successive heat stresses can either amplify the negative effects of late stresses or alleviate these late effects (the so-called ‘priming effect’ of first stress exposure) as a consequence of stress memory-triggered processes (Magno et al. 2022ab, Hilker, 2019). The objective was to test whether crop model such as SuMoToRI was able to predict such non-additive effects of recurring heat stresses without additional modification of the model.
2. Materials and methods
Firstly, the initial version of the model (SuMoToRI, Brunel-Muguet et al. 2015) was implemented to predict seed yield and quality criteria i.e. seed oil and protein contents (SuMoQuality, Magno et al. 2022b). The extension of the period of prediction required to (i) consider the pods for the equations related to light interception and carbon assimilation, as they act as the main photosynthetic organ throughout the seed filling and maturation phases and (ii) define two periods (in thermal time) that distinguish pods’ heterotrophy from pods’ autotrophy. The seed oil content was estimated as a function of the post flowering intercepted Photosynthetically Active Radiation (PARi, Aguirrezabal et al. 2015) and the seed protein content was negatively correlated to the seed oil content (Hammac 2017).
Secondly, the SuMoQuality model was tested with different heat stress sequences previously used in an in a greenhouse assay (Figure 1, Magno et al. 2022a). These sequences were initially designed to observe any effect of an early mild stress (EMS) on the magnitude of the effect of later heat peaks (LHP).
3. Results
For each variable, the simulation values were of the same order of magnitude as the observations’ values (Figure 2). The model was able to predict the non-additive effects of repeated heat stresses. Indeed, the observed data indicated that seed yield was reduced by 22.1% under RSS and by -37.7% under EMS and its was similar to the control under 4LPH which highlighted that the individual effects of EMS and LHP were not additive. Similarly, the simulation output indicated a reduction of 2.9% under RSS, 2.9% under EMS and 1.4% under LHP, which also pointed out non-additive effects of the individual stresses (EMS and LHP). The same conclusions were drawn for oil and protein contents. However, while the decrease in oil concentration under RSS was correctly predicted under RSS (-22% for the observations vs. -25% for the simulations), the increase in protein contents was not with almost to a two-fold difference (+33% for the observations vs. + 53% for the simulations).
4. Discussion
These exploratory results indicated that the SuMoQuality model was able to predict seed yield and main quality criteria in oilseed rape although the seed oil content was slightly overestimated in the control condition. Under heat stress, trends (increase or decrease) were nicely represented but the model tend to fail simulating correct increase in protein content under repeated heat stresses. These conclusions indicate that no additional equations nor structural changes were needed to predict the effects of repeated stresses. The prediction quality is likely to be improved with additional datasets to better calibrate oil and protein content related-parameters.
- References
Aguirrezábal et al. 2015. doi.org/10.1016/B978-0-12-417104-6.00017-0
Brunel-Muguet et al. 2015. doi: 10.3389/fpls.2015.00993
Hammac et al. 2017. doi: 10.1021/acs.jafc.7b02778
Hilker and Schmülling 2019. doi: 10.1111/pce.13526
IPCC, 2021. doi:10.1017/9781009157896.001
Magno Massuia de Almeida et al. 2022a. doi: 10.1016/j.plantsci.2022.111559
Magno Massuia de Almeida et al. 2022b. PhD thesis, 307 pp.
Poisson et al. 2018. doi: 10.1016/j.eja.2018.05.001
| Keywords | modelling; heat stress; oilseed rape; stress memory; crop model |
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