Speaker
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Introduction: Adapting crops to climate will be challenged by shifting environments and increasing weather instability impacting both yield potential and stability. Exploring genotype x environment interactions (GEI) sources at large scale to develop outperforming and stable genotypes is an important step. Defining the Target Population of Environments (TPE) across the crop production area – i.e., set of climatic scenarios minimizing GEI – is an approach to identify the specific or broad adaptation of a genotype to climatic scenarios. Crops models are powerful tools to accurately characterize crop environments and the origin of GEI (Chenu et al., 2011). Spring malting barley is a cereal crop largely understudied in plant ecophysiology. This biological model is distributed worldwide and cultivated under contrasting agro-climatic conditions, with a short cycle, lending vulnerability to climate change. Our study aimed to (i) highlight the main eco-climatic factors – climatic variables calculated between two growth stages – driving yield levels and GEI for yields and (ii) define the spring malting barley European Target Population of Environments (TPE) for crop adaptation.
Materials and Methods: Yield data from 2015 to 2022 were collected from an European multi-environment trials (MET) network. The phenology-calibrated CERES-Barley model (DSSAT) was used to calculate 91 eco-climatic factors from historical weather data to characterize each environment of the MET. Partial Least Squares (PLS) regression analyses were carried out to identify the main eco-climatic factors impacting yield levels and relative yields of genotypes across Europe (Elmerich et al., 2023). An environmental classification was performed based on the GEI-drivers across 1,450 environments, including tested and untested locations within the European area of production to define climatic scenarios minimizing GEI.
Results: Water stress was not identified as a major yield-driver. Results suggested an important contribution of cool temperatures at early stages to explain yields variation sources across the MET. Strong regional contrasts in the critical phenological stages for yield levels were observed. The grain filling period had the lowest influence on yields. Eco-climatic factors driving GEI differed from those of yields. Elevated temperatures during stem elongation, solar radiation and drought during grain filling shown a high contribution to GEI. Thermal amplitude around anthesis also emerged as influent. Three main environment types (ETs) were identified from the GEI-drivers and contrasted in their patterns of temperatures during vegetative growth, solar radiation intensity, and water stress during grain filling. The frequency of occurrence differed in time and space across Europe (Figure 1). Heterogeneity in genotype adaptations to environment types were observed, with genotypes having specific adaptation to one environment type while others with broad adaptation.
Discussion: Without limiting assumptions, this approach clarified the environmental sources of inter and intra-annual variability in yields. To adapt to climate change, agricultural practices will need to evolve to minimize exposure to adverse climatic factors during critical growth phases. Shift sowing can be used, but the crop may be exposed to potential critical factors during the short spring-summer season, which includes cold stress or waterlogging. The choice of adapted cultivars will also be a key decision, as their sensitivity to climatic factors differs. The drivers of GEI contrasted with those of yields, allowing the identification of three major environment types minimizing GEI. Performances of the existing germplasm across the TPE showed contrasted responses that can be directly used for product positioning. This work will help breeders to cope with GEI for spring barley breeding, by weighing trials using MET-TPE alignment, defining more efficient trial networks and designing ideotypes for specific or broad adaptation (Cooper et al., 2022).
References:
Chenu, K., Cooper, M., Hammer, G.L., Mathews, K.L., Dreccer, M.F., Chapman, S.C., 2011. Environment characterization as an aid to wheat improvement: interpreting genotype–environment interactions by modelling water-deficit patterns in North-Eastern Australia. Journal of Experimental Botany 62, 1743–1755. https://doi.org/10.1093/jxb/erq459
Cooper M, Messina CD, Tang T, Gho C, Powell OM, Podlich DW, Technow F, Hammer GL. 2023a. Predicting genotype × environment × management (G×Ex×M) interactions for design of crop improvement strategies: integrating breeder, agronomist, and farmer perspectives. Plant Breeding Reviews 46, 467–585. https://doi.org/10.1002/9781119874157.ch8
Elmerich, C., Faucon, M.-P., Garcia, M., Jeanson, P., Boulch, G., Lange, B., 2023. Envirotyping to control genotype x environment interactions for efficient soybean breeding. Field Crops Research 303, 109113. https://doi.org/10.1016/j.fcr.2023.109113
| Keywords | Target Population of Environments; Multi-Environmental Trials; Crop modeling; Climatic factors; Envirotyping |
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