Introduction: Breeding barley cultivars adapted to drought requires in-depth knowledge on physiological drought responses.Methods: We used a high-throughput functional phenotyping platform to examine the response of four high-yielding European spring barley cultivars to a standardized drought treatment imposed around flowering.Results: Cv. Chanell showed a non-conserving water-use behavior with high transpiration and maximum productivity under well-watered conditions but rapid transpiration decrease under drought. The poor recovery upon re-irrigation translated to large yield losses. Cv. Baronesse showed the most water-conserving behavior, with the lowest pre-drought transpiration and the most gradual transpiration reduction under drought. Its good recovery (resilience) prevented large yield losses. Cv. Formula was less conserving than cv. Baronesse and produced low yet stable yields. Cv. RGT’s dynamic water use with high transpiration under ample water supply and moderate transpiration decrease under drought combined with high resilience secured the highest and most stable yields.Discussion: Such a dynamic water-use behavior combined with higher drought resilience and favorable root traits could potentially create an ideotype for intermediate drought. Prospective studies will examine these results in field experiments and will use the newly gained understanding on water use in barley to improve process descriptions in crop simulation models to support crop model–aided ideotype design.

Sorghum holds the potential for enhancing food security, yet the impact of the interplay of water stress and salinity on its growth and productivity remains unclear. To address this, we studied how drought and salinity affect physiological traits, water use, biomass, and yield in different tropical sorghum varieties, utilizing a functional phenotyping platform, Plantarray. Cultivars (Kuali, Numbu, Samurai2) were grown under moderate and high salinity, with drought exposure at booting stage. Results showed that Samurai2 had the most significant transpiration reduction under moderate and high salt (36% and 48%) versus Kuali (22% and 42%) and Numbu (19% and 16%). Numbu reduced canopy conductance (25% and 15%) the most compared to Samurai2 (22% and 33%) and Kuali (8% and 35%). In the drought*salinity treatment, transpiration reduction was substantial for Kuali (54% and 57%), Samurai2 (45% and 60%), and Numbu (29% and 26%). Kuali reduced canopy conductance (36% and 53%) more than Numbu (36% and 25%) and Samurai2 (33% and 49%). Biomass, grain yield, and a-100 grain weight declined in all cultivars under both salinity and drought*salinity, and Samurai2 was most significantly affected. WUE_biomass significantly increased under drought*salinity. Samurai2 showed reduced WUE_grain under drought*salinity, unlike Kuali and Numbu, suggesting complex interactions between water limitation and salinity in tropical sorghum.

Crop model-aided ideotyping can accelerate the breeding of resilient barley cultivars. Yet, the accuracy of process descriptions in the crop models still requires substantial improvement, which is only possible with high-quality (HQ) experimental data. Despite being demanded frequently, such data are still rarely available, especially for Northern European barley production. This study is one of the first to contribute to closing this existing data gap through the targeted collection of HQ experimental data in pluri-annual, multi-location spring barley field trials in Denmark. With this data, the prediction accuracy of Agricultural Production Systems SIMulator significantly increased in contrast to commonly utilized lower quality datasets. Using this data for model calibration resulted in more accurate predictions of in-season plant development and important state variables (e.g. final grain yield and biomass). The model’s prediction accuracy can ultimately be further improved by examining remaining model weaknesses that were discoverable with the HQ data. Process descriptions regarding, for example, early and late leaf development, soil water dynamics and respective plant response appeared to require further improvement. By illustrating the effect of data quality on model performance we reinforce the need for more model-guided field experiments.

Process-based crop simulation models (CSMs) are valuable tools for assessing genotype by environment by management (GxExM) interactions and quantifying climate change impacts on crops. Ex-ante evaluations of adaptation options to drought stress require well-validated CSMs that are continuously improved and evaluated. This asks for high quality data from model-driven field experiments. We collected detailed data on weather, soil, and crop growth and development in one season of barley (cv. RGT Planet) field experiments at three locations in Denmark. The resultant dataset meets the highest standards for crop model improvement as defined by the modelling community. To evaluate the importance and impact of data quality on model calibration results, the CSM APSIM was calibrated for one location, first with a low, then with a medium, and finally with the high quality dataset generated in the field experiments. The low quality dataset represents a typical scenario of limited data availability for CSM calibration (e.g. limited soil description, few in-season phenology and biomass measurements). In a medium quality dataset usually better soil descriptions and phenology and biomass measurements at different crop stages are available, yet in lower temporal and spatial resolution than in a high quality dataset.Phenology was predicted accurately with all datasets, but the highest accuracy was achieved using the high quality dataset (root mean square error RMSE low: 4.39, medium: 4.23, high: 1.56). LAI was overestimated with all quality datasets; however, the high quality calibration results were closest to the observations (RMSE low: 1.89, medium: 1.61, high: 1.09). Final grain yield was underestimated with the low and medium quality dataset but slightly overestimated with the high quality dataset, which facilitated the most accurate yield prediction (difference between modelled and observed yield: low: -6%, medium: -3.13 %, high: +1.38%).                                                                  Findings from this study support our basic hypothesis that calibrating a CSM with high quality data increases the prediction accuracy.    However, our results show that calibrating LAI and grain yield (complex traits) require more comprehensive datasets than calibrating phenology.By generating such a high quality dataset, we contribute substantially to meeting the need for detailed and comprehensive datasets fit for model calibration and evaluation purposes, which are especially rare for northern Europe. We also found that APSIM possibly does not fully reproduce translocation processes, but this requires further field and modelling experiments.

Compared with other cereals, rye ( Secale cereale L.) has traits that contribute to superior drought tolerance, thus making it a suitable alternative under intensifying droughts related to climate change. The distributions of these traits across a range of varieties and their relationships with yield potential have yet to be fully elucidated. Therefore, the sensitivity of 20 varieties, which differ in origin and age, to drought stress between the second half of stem elongation and the beginning of grain filling was analyzed. Potted plants were exposed to drought on an automated phenotyping platform, enabling accurate simulation of water loss for all varieties. Soil moisture was reduced to 30% of the soil water capacity, followed by rewetting. Although traditional varieties exhibited greater stability of key yield components under drought stress, modern varieties achieved higher absolute yields under both drought and well-watered conditions. Therefore, traditional varieties cannot replace modern varieties but remain valuable donors of drought resistance traits.