Editorial: Photosynthesis under abiotic stress
Notice bibliographique
Résumé
Abiotic stress factors such as drought, salinity, extreme temperatures, UV radiation, high light, nutrient deficiency are the main reasons for the reduction of crop yields and food production worldwide (Kopecká et al., 2023;Moustakas, 2025). Photosynthesis is the device of crop productivity, but likewise, it is a complex process that is extremely responsive to various abiotic stresses with a multifaceted relationship to the growth and productivity of plants and aquatic photosynthetic organisms, such as algae and cyanobacteria (Gururani et al., 2015). As a result of drought stress, for example, remarkable changes in growth, photosynthesis, enzymatic activities, and biomass production, occur (Croce et al., 2024). In plants, the decreased photosynthetic efficiency, which is linked to both stomatal and non-stomatal limitations, is the result of a disruption of either biochemical or/and photochemical activity and increased oxidative damage by the surplus reactive oxygen species (ROS) accumulation, which can harm the chloroplast, and particularly photosystem II (PSII) (Moustakas et al., 2022b). However, plants have developed several effective approaches at the morphological, physiological, and biochemical levels, allowing them to avoid and/or tolerate drought stress (Moustakas, 2025).Photosynthesis of food crops under environmental stress conditions has been considered to be a real challenge for scientists and crop breeders in order to fulfil the huge demand for food in the world (Morales et al., 2020;Croce et al., 2024). The fast progress of synthetic biology tools now offers new scenarios towards totally new designs of improved photosynthetic systems and adjusting photosynthesis to the increasing demands of our changing climate (Zhu et al., 2022;Croce et al., 2024). Photosynthetic manipulation offers new prospects for enhancing crop yield (Zhu et al., 2022). 37 Therefore, detailed information organism and a better understanding of 38 the photosynthetic machinery to environmental stresses could help in developing crops with higher 39 yields (Muhammad al., 2021). Manipulating photosynthetic organisms with enhanced abiotic stress 40 tolerance will involve a complete understanding of ROS signaling and the regulatory functions of 41 several other components, including secondary metabolites, transcription factors, phytohormones, and 42 protein kinases, in the responses of photosynthetic apparatus to abiotic stress (Gururani et al., 2015). 43To meet global food and feed requirements, considering current climate change scenarios, it is 44 essential to recognize how photosynthetic organisms respond and adapt their metabolism to abiotic 45 stress (Zhu et al., 2022;Leister, 2023;Wani, 2023;Croce et al., 2024) Global crop production faces rising hazards from the increased frequency, intensity and duration, 58 of drought stress incidents owing to climate change, and its effects when combined with other stress 59 becomes more noticeable (Moustaka et al., 2025). Chlorophyll fluorescence analysis has been 60commonly used to evaluate photosynthetic function and to assess plant tolerance to different 61 environmental stresses (Moustakas et al., 2022a). Naseer et al. by using chlorophyll fluorescence 62 analysis revealed that both reduced irrigation and increased shading durations negatively impact winter 63 wheat during the grain-filling stage, with interactive effects causing the most severe damage to 64 physiological functions and leading to significant yield reductions. However, the combined effects of 65 abiotic stresses do not always outcome in a simple additive response, but rather, they may produce 66 complex and interconnected physiological and molecular mechanisms (Moustaka et al., 2025) The dramatic decrease in atmospheric CO2 concentration during Oligocene was directly linked 82 to evolution of C4-type photosynthesis (Ehleringer et al., 1991). Miao et al. reported that under low 83 CO2 conditions, C3 plants like Arabidopsis thaliana refurbish their metabolism to recycle ammonium 84 by increasing the expression of most genes encoding the C4-related enzymes and transporters, genes that involved in photorespiration, and genes that are involved in ammonium refixation. They 86 proposed an "evolutionary hitchhiking" process, where the necessary metabolic adjustments for 87 ammonium low CO2 conditions co-opted expression of C4-related genes that were 88 already present in the C3 genome. 89Soil salinization is one of the main constraints to crop production in arid and semi-arid regions. 90 Zhou et al. explore the efficacy of combined application of organic and inorganic nitrogen as a valuable 91 strategy to improve the productivity of maize in saline soils. Through series of carefully planned 92 photosynthetic and biochemical experiments the authors concluded that organic and inorganic nitrogen 93 application mitigates salinity stress effects on maize. In mildly saline soils, inorganic nitrogen 94 application (U1) and organic nitrogen replacing 50% (O1), was optimal and improved yield primarily 95 through enhanced photosynthetic performance, whereas in moderately saline soils O1 nitrogen 96 application was optimal and yield formation was driven by an integrated influence of growth traits, 97 photosynthetic parameters, and catalase (CAT) activity. The results provide insights and better 98 understanding of nitrogen forms management in improving crop productivity in saline environments. 99The effects of nitrogen as the most essential and key limiting nutrient factor for plant growth and 100 overall plant development were also studied by Li et al. The above studies clearly show that photosynthesis under environmental stress conditions is a 119 real challenge for scientists that must find methods to decrease the harmful impacts on crop 120 productivity. 121
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,010 | 0,013 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,040 | 0,035 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».