Interannual and local fluctuations in wheat crop yield are mostly explained by abiotic constraints. Heatwaves and drought, which are among the top stressors, commonly co-occur, and their frequency is increasing with global climate change. High-throughput methods were optimized to phenotype wheat plants under controlled water deficit and high temperature, with the aim to identify phenotypic traits conferring adaptative stress responses. Wheat plants of 10 genotypes were grown in a fully automated plant facility under 25/18 °C day/night for 30 d, and then the temperature was increased for 7 d (38/31 °C day/night) while maintaining half of the plants well irrigated and half at 30% field capacity. Thermal and multispectral images and pot weights were registered twice daily. At the end of the experiment, key metabolites and enzyme activities from carbohydrate and antioxidant metabolism were quantified. Regression machine learning models were successfully established to predict plant biomass using image-extracted parameters. Evapotranspiration traits expressed significant genotype–environment interactions (G×E) when acclimatization to stress was continuously monitored. Consequently, transpiration efficiency was essential to maintain the balance between water-saving strategies and biomass production in wheat under water deficit and high temperature. Stress tolerance included changes in carbohydrate metabolism, particularly in the sucrolytic and glycolytic pathways, and in antioxidant metabolism. The observed genetic differences in sensitivity to high temperature and water deficit can be exploited in breeding programmes to improve wheat resilience to climate change.

Increasing temperatures and extended drought episodes are among the major constraints affecting food production. Maize has a relatively high temperature optimum for photosynthesis compared to C₃ crops, however, the response of this important C₄ crop to the combination of heat and drought stress is poorly understood. Here, we hypothesized that resilience to high temperature combined with water deficit (WD) would require efficient regulation of the photosynthetic traits of maize, including the C₄–CO₂ concentrating mechanism (CCM). Two genotypes of maize with contrasting levels of drought and heat tolerance, B73 and P0023, were acclimatized at high temperature (38°C versus 25°C) under well-watered (WW) or WD conditions. The photosynthetic performance was evaluated by gas exchange and chlorophyll a fluorescence, and in vitro activities of key enzymes for carboxylation (phosphoenolpyruvate carboxylase), decarboxylation (NADP-malic enzyme), and carbon fixation (Rubisco). Both genotypes successfully acclimatized to the high temperature, although with different mechanisms: while B73 maintained the photosynthetic rates by increasing stomatal conductance (gs), P0023 maintained gs and showed limited transpiration. When WD was experienced in combination with high temperatures, limited transpiration allowed water-savings and acted as a drought stress avoidance mechanism. The photosynthetic efficiency in P0023 was sustained by higher phosphorylated PEPC and electron transport rate (ETR) near vascular tissues, supplying chemical energy for an effective CCM. These results suggest that the key traits for drought and heat tolerance in maize are limited transpiration rate, allied with a synchronized regulation of the carbon assimilation metabolism. These findings can be exploited in future breeding efforts aimed at improving maize resilience to climate change.

Plant breeders and plant physiologists are deeply committed to high throughput plant phenotyping for drought tolerance. A combination of artificial intelligence with reflectance spectroscopy was tested, as a non-invasive method, for the automatic classification of plant drought stress. Arabidopsis thaliana plants (ecotype Col-0) were subjected to different levels of slowly imposed dehydration (S0, control; S1, moderate stress; S2, severe stress). The reflectance spectra of fully expanded leaves were recorded with an Ocean Optics USB4000 spectrometer and the soil water content (SWC, %) of each pot was determined. The entire data set of the reflectance spectra (intensity vs. wavelength) was given to different machine learning (ML) algorithms, namely decision trees, random forests and extreme gradient boosting. The performance of different methods in classifying the plants in one of the three drought stress classes (S0, S1 and S2) was measured and compared. All algorithms produced very high evaluation scores (F1 > 90%) and agree on the features with the highest discriminative power (reflectance at ~670 nm). Random forests was the best performing method and the most robust to random sampling of training data, with an average F1-score of 0.96 ± 0.05. This classification method is a promising tool to detect plant physiological responses to drought using high-throughput pipelines.

Fungal infections remain a major health concern with aromatic plants and their metabolites standing out as promising antifungal agents. The present study aims to assess, for the first time, the antifungal and anti-inflammatory potential of Bupleurum subsp. paniculatum (Brot.) H.Wolff essential oil from Portugal. The oil obtained by hydrodistillation and characterized by GC-MS, showed high amounts of monoterpene hydrocarbons, namely α-pinene (29.0–36.0%), β–pinene (26.1–30.7%) and limonene (10.5–13.5%). The antifungal potential was assessed, according to CLSI guidelines, against several clinical and collection strains. The essential oil showed a broad fungicidal effect being more potent against Cryptococcus neoformans and dermatophytes. Moreover, a significant germ tube inhibition was observed in Candida albicans as well as a disruption of mature biofilms, thus pointing out an effect of the oil against relevant virulent factors. Furthermore, fungal ultrastructural modifications were detected through transmission electron microscopy, highlighting the nefarious effect of the oil. Of relevance, the oil also evidenced anti-inflammatory activity through nitric oxide inhibition in macrophages activated with lipopolysaccharide. In addition, the essential oil’s bioactive concentrations did not present toxicity towards macrophages. Overall, the present study confirmed the bioactive potential of B. rigidum subsp. paniculatum essential oil, thus paving the way for the development of effective drugs presenting concomitantly antifungal and anti-inflammatory properties.

Plants are increasingly exposed to events of elevated temperature and water deficit, which threaten crop productivity. Understanding the ability to rapidly recover from abiotic stress, restoring carbon assimilation and biomass production, is important to unravel crop climate resilience. This study compared the photosynthetic performance of two Triticum aestivum L. cultivars, Sokoll and Paragon, adapted to the climate of Mexico and UK, respectively, exposed to 1‐week water deficit and high temperatures, in isolation or combination. Measurements included photosynthetic assimilation rate, stomatal conductance, in vitro activities of Rubisco (EC 4.1.1.39) and invertase (INV, EC 3.2.1.26), antioxidant capacity and chlorophyll a fluorescence. In both genotypes, under elevated temperatures and water deficit (WD38°C), the photosynthetic limitations were mainly due to stomatal restrictions and to a decrease in the electron transport rate. Chlorophyll a fluorescence parameters clearly indicate differences between the two genotypes in the photoprotection when subjected to WD38°C and showed faster recovery of Paragon after stress relief. The activity of the cytosolic invertase (CytINV) under these stress conditions was strongly related to the fast photosynthesis recovery of Paragon. Taken together, the results suggest that optimal sucrose export/utilization and increased photoprotection of the electron transport machinery are important components to limit yield fluctuations due to water shortage and elevated temperatures.