BR signaling mitigates Cu toxicity in rice by integrating ROS homeostasis with copper translocation.

Multi-scale cotton yield prediction using machine learning and Sentinel-2 imagery

Concentration and flux-based risk assessments reveal large losses in mung bean production due to current ozone pollution in Rajasthan, India.

Field-scale potato yield prediction from sentinel-2 time series using lightweight deep learning models

Agronomic management practices shape soil health and crop productivity in northeastern China: Insights from a meta-analysis

The application of crop residues is a common agricultural practice used to enhance soil health. However, the use of low-quality crop residues can lead to nitrogen (N) depletion in soil due to microbial immobilisation. This microbial activity is influenced by many factors including residue quality, soil type and interactions with higher trophic levels such as microarthropods. To investigate the processes influencing the effectiveness of crop residue application, we conducted a field mesocosm experiment using barley plants grown in either chernozem or sandy soil. As a second factor, low-quality crop residue was added to half of the mesocosms. All soils were initially defaunated, with half subsequently refaunated. Over a three-month period, we measured barley biomass and N content, soil microbial activity, and soluble and soil N concentrations. Inorganic N dynamics were more pronounced in chernozem soil, while differences in crop yield and plant N content were more evident in sandy soil. Residue addition stimulated microbial activity. Although it did not directly deplete soil nitrate, its negative effects were reflected in reduced crop yield and lower leaf N content. Faunal presence significantly reduced nitrate leaching and increased soil nitrate concentrations in both soil types. Moreover, faunated mesocosms exhibited higher microbial activity and crop yield compared to defaunated ones. These findings highlight the critical role of soil microarthropods in mediating the effects of crop residue on plant performance. Thus, maintaining functional soil faunal communities is essential for improving nitrogen retention and crop productivity in residue-amended agricultural systems. • Soil fauna modulate effects of low-quality crop residues in chernozem and sandy soils. • Low-quality crop residue decreases crop yield and plant N content. • Soil fauna increase crop yield and soil nitrate content, and reduce nitrate leaching. • Effects of low-quality residues and soil fauna differ between the two soil types.

Excessive rainfall anomalies during the dry season (harvest period) and their impact on rainfed crop yields in Ethiopia.

Climate-adaptive restructuring of nitrogen inputs to mitigate global trade-offs among fertilizer use, crop yield, and N₂O emissions

A LEA2 domain-containing protein PpLEA37 promotes seed germination under salinity and drought stresses through abscisic acid signaling.

Metabolic engineering of microbial pathways for 5-aminolevulinic acid biosynthesis: Recent advances and biotechnological applications.

SnRK1 subcellular localization is linked to TOR signaling, chloroplast function, and salt stress tolerance in mature Arabidopsis leaves.

Plant phytopathogenic fungi (PF) pose a significant threat to global agriculture by reducing crop yields and quality. The excessive use of chemical synthetic fungicides has raised concerns about environmental pollution and human health risks, while simultaneously accelerating the emergence of pathogen resistance. In this study, we aimed to discover antifungal natural products (NPs) from fungal metabolites, which represent promising eco-friendly alternatives, via bioactivity-guided isolation. Two novel hybrid polyketides, novomycins A and B, were isolated from Aspergillus novofumigatus J2-22. Novomycin B demonstrated broad-spectrum antifungal activity against eight PF strains, with half-maximal effective concentration (EC50) values ranging from 0.84 to 53.32 μg mL-1. Notably, novomycin B exhibited superior antifungal activity compared to the agricultural fungicide Hymexazol, achieving 100% inhibition of Botryosphaeria dothidea at 50 μg mL-1. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) analyses revealed that novomycin B treatment caused severe destruction to the cell membrane, as evidenced by measurements of electrical conductivity, soluble protein content, and ergosterol content. Besides, novomycin B inhibited the activities of antioxidant enzymes (superoxide dismutase and catalase) in B. dothidea, leading to accumulation of hydrogen peroxide and malondialdehyde, which further exacerbated membrane system damage. Transcriptomic data indicated that novomycin B severely impaired DNA replication systems and activated extensive DNA damage repair mechanisms, which was further supported by TUNEL and EdU incorporation assays. Furthermore, it impaired energy metabolism, as confirmed by the inhibition of ATPase activity and decreased adenosine triphosphate (ATP) content. These results demonstrate that novomycin B, as a natural fungicide exhibiting multi-target antifungal mechanisms, provides a theoretical foundation for its development as an agricultural fungicide. © 2026 Society of Chemical Industry.

Breaking the bloom: Exploring the genetic pathways in crop plants from primordia formation to seed development.

With the continuous deterioration of the global environment, plants are facing various abiotic stresses that adversely affect plant growth and crop yield. In this study, we identified a lysophosphatidic acid acyltransferase gene CcLPAT2 from hickory that can increase plant oil content, and also found that its transcription is strongly induced by multiple abiotic stressors. However, the molecular mechanisms underlying the plant responses to multiple abiotic stressors are poorly understood. We demonstrated that CcLPAT2 localizes to the endoplasmic reticulum (ER) and contributes to the regulation of fatty acid metabolism, including changes in C18:1 and C18:2 levels, and membrane lipid composition, thereby promoting membrane integrity and stabilizing reactive oxygen species (ROS) homeostasis under stress conditions. Moreover, we found that the transcription factor CcMYB5 directly binds to the promoter of CcLPAT2 and promotes its expression under multiple abiotic stress conditions; interestingly, the expression of CcMYB5 is also induced by multiple abiotic stressors. Together, our genetic and biochemical analyses reveal a novel regulatory mechanism in which CcLPAT2-mediated changes in fatty acids and membrane lipids protect plant cells from abiotic stresses by reducing ROS accumulation and enhancing membrane stability.

Soybean is one of the most important agricultural cropsworldwideandservesasamajorsourceoffood,edible oil, and animal feed. The productivity and quality of soybean cropsaresignificantlyaffectedbyvariousleafdiseasessuch as Caterpillar damage, Rust, Bacterial Blight, and Diabrotica Speciosa. These diseases reduce crop yield and negatively impact agricultural production and the economic condition of farmers. Therefore, early and accurate disease identification is essential foreffectivecropmanagementandimprovedproductivity.Tradi-tionaldiseasedetectionmethodsmainlyrelyonmanualinspection by agricultural experts, which is time-consuming, costly, less efficient for large-scale farming, and prone to human error. This paper presents an automated soybean leaf disease detec-tionsystemusingimageprocessinganddeeplearningtechniques. The proposed system utilizes Convolutional Neural Networks (CNN) for automatic feature extraction and classification of healthyanddiseasedsoybeanleaves.ARandomForestclassi-fierisalsoimplementedforcomparativeanalysistoevaluate the performance of traditional machine learning approaches.The collected dataset undergoes preprocessing steps such as image resizing, normalization, filtering, and data augmentation techniques including rotation, zooming, and flipping to improve model performance and reduce overfitting. Experimental results demonstrate that the CNN-based model achieves higher accuracy, precision, recall, and F1-score comparedtoRandomForestmethods.Theproposedsystemprovides an efficient, reliable, and cost-effective solution for real-time soybean leaf disease detection. It can support smart agriculture systems and assist farmers in taking timely preventive measures to minimize crop damage and improve overall agricultural productivity.

Fungal pathogens exert a profound influence on agricultural productivity, contributing substantially to crop losses during germination, vegetative growth, and post-harvest storage. Among these, species of Fusarium are recognized as some of the most pervasive and destructive phytopathogens, causing diseases such as rhizome rot and vascular wilt, which markedly diminish crop yield and quality. The impact of fungal infections is particularly severe during the germination phase, where they compromise seed viability, reduce stand establishment, and ultimately impair overall productivity. Conventional management strategies relying on synthetic fungicides, although effective, are increasingly associated with environmental hazards, pathogen resistance, and adverse effects on beneficial soil microbiota. Consequently, there is a pressing need to develop eco-friendly and sustainable alternatives for fungal disease management. In this context, the present investigation evaluates the antifungal efficacy of Tridax procumbens extracts, a readily available medicinal plant species, against pathogenic Fusarium spp. isolated from infected ginger crops in agricultural fields. Extracts were prepared from T. procumbens collected from the Melghat forest region, and their bioactivity was assessed using agar-based in vitro antifungal assays. Results demonstrated a pronounced inhibitory effect on fungal growth, with ethanolic extracts exhibiting the highest suppressive potential, followed by aqueous extracts. The application of such bioactive plant-derived formulations as seed treatments offers a promising strategy for mitigating fungal infections during germination while safeguarding soil microbial communities. The results findings highlight T. procumbens’ potential as a sustainable bio-control agent, helping to increase crop establishment along with productivity in the production of ginger.

Long-term monocropping can lead to an imbalance in soil nutrients and an increase in soil-borne diseases, which consequently cause significant reductions in crop yield. In the alpine regions of Korea, the production of Kimchi cabbage (Brassica rapa subsp. pekinensis (Lour.) Hanelt) faces increasing challenges. The scarcity of arable land has led to repeated monoculture practices. Additionally, rising temperatures and shallow, stony soils characteristic of sloped fields further limit sustainable cultivation. Although extensive research has examined the physicochemical differences in soil across various cropping systems, our understanding of rhizosphere microbial communities in Kimchi cabbage remains limited. In this study, we investigated the effects of three cropping systems - monoculture, rotation with soybean, and rotation with potato - on rhizosphere microbial structure in both field and pot conditions over nine or ten successive cropping cycles. The application of high-throughput sequencing to the analysis of bacterial 16S rRNA and fungal ITS regions revealed significant effects of both cropping system and sequence on rhizosphere communities. The cropping sequence had the most significant influence on the microbial communities, accounting for 14% of the observed variance. Long-term cultivation was associated with reduced microbial diversity, particularly in the monoculture system. Community dissimilarity declined more rapidly under the monoculture system than under the rotation systems. Continuous Kimchi cabbage cultivation exhibited an increase in the abundances of Rhodanobacter, Ktedonobacteraceae, and Alternaria in comparison to the rotation treatments. The abundances of Sphingomonas, Terrabacter, Pseudarthrobacter, Trichocladium, and Botryotrichum decreased along the cropping sequence in the rhizosphere soil of the monoculture system. Although the cropping system had a modest effect, specific microbial groups were associated with monoculture cultivation. The cropping sequence exhibited a marked cumulative effect on the rhizosphere microbial diversity of Kimchi cabbage across all treatments. These findings provide a microbiota-based rationale for sustainable management strategies in Kimchi cabbage production systems.

Crop yield and quality are influenced by root morphology, but the impact of root morphology on cherry tomatoes under Ethylene-vinyl acetate copolymer (EVA) film mulching (EUV treatment) with Eu3⁺-VTB450 light converter remains unclear. In this study, "Meiying No. 2" cherry tomato was selected as the experimental material, and a one-factor randomized block field experiment with conventional EVA film mulching (CK) and EUV treatment was conducted to investigate the effects of EUV treatment on the yield and quality of the crop. The results showed that EUV treatment induced reconstruction of the light spectral composition, resulting in a 17.0% and 13.5% increase in blue light and red-orange light proportions, respectively (P < 0.05). Compared with CK, EUV treatment improved root development, SPAD values, chlorophyll fluorescence parameters, and gas exchange parameters of cherry tomatoes at each growth stage, with agronomic traits improved to varying degrees. The contents of lycopene, soluble sugar, soluble solids, soluble protein, and vitamin C in fruits increased by 20.4%, 15.9%, 36.6%, 36.5%, and 13.4%, respectively, and the harvest yield increased by 33.1%. Furthermore, partial least squares path modeling revealed that EUV treatment optimized root growth by regulating the light spectral composition, which in turn enhanced plant photosynthesis and growth, ultimately leading to comprehensive improvements in yield and quality. In conclusion, this study systematically investigated the effects of EUV on the growth physiology of cherry tomatoes, while providing novel solutions for light quality regulation in horticultural plants.

Drought tolerance is an important breeding objective for improving the yield of cereal crops. Identifying key drought-tolerant loci/genes from Setaria viridis and Setaria italica is essential for enhancing the yield and stress resistance of foxtail millet via molecular breeding technologies. Here, an interspecific recombinant inbred line (RIL) population was constructed by crossing the S. italica cultivar Yugu1 with the wild S. viridis H1. Phenotypic evaluation and QTL mapping for drought tolerance were conducted from the grain filling to the maturity stage. Five strong drought-tolerant lines were screened using the drought resistance index (DRI). Phenotypic variance analysis indicated that the period of duration was the key factor affecting drought tolerance in the population. Furthermore, the RIL population was subjected to whole-genome sequencing, and a high-density linkage map was constructed, comprising 3,029 bin markers spanning 709.34cM with an average interval of 0.24cM. A total of 10 QTLs were detected for panicle number per plot (PNP), grain weight per plant (GWP), and 1000-grain weight (TGW). Eight QTLs were associated with PNP, and the favorable alleles at all loci except qPNP4.1 were derived from Setaria viridis H1. The favorable alleles of the other two QTLs for GWP and TGW were all from Yugu1. Based on functional homology alignment, five genes located within three PNP-related QTL intervals (Seita.5G420900, Seita.7G306400, Seita.5G312700, Seita.5G319000, Seita.5G319500) were predicted to modulate the tillering, grain yield under drought stress conditions. These findings provide an important basis for drought-tolerant breeding and the dissection of the drought tolerance mechanisms in foxtail millet and its close species.

Paddy leaf disease (PLD) detection has grown more difficult, yet early detection might prevent significant losses due to decreased crop yield. However, existing models struggle to accurately classify diseases under difficult circumstances like intricate backgrounds, fluctuating lighting, and overlapping leaves. Additionally, existing models do not incorporate efficient optimization strategies, leading to suboptimal accuracy and poor generalization on unseen data. To address these challenges, a novel deep learning-based YOLO-LEAFNET method for PLD detection utilizing IGT-YOLO, integrating the YOLOv8 disease detection with the Improved Gorilla Troops (IGT) optimization. The input paddy leaf images are pre-processed using Bilateral Contrast Limited Adaptive Histogram Equalization (B-CLAHE) to enhance image quality and improve local contrast while preserving disease boundaries. YOLOv8 model is utilized to detect and classify paddy leaf diseases by accurately localizing affected regions with bounding boxes. Then, the IGT algorithm boosts the disease detection accuracy by optimizing YOLOv8 through effective hyperparameter tuning. The proposed YOLO-LEAFNET method effectiveness was evaluated using recall, F1 score, specificity, accuracy, and precision. B-CLAHE enhanced noise-free images improve contrast and detection accuracy, while the IGT-YOLO model ensures scalable, efficient early diagnosis of paddy leaf diseases with 99.07% accuracy. The YOLO-LEAFNET enhanced the total accuracy by 3.21%, 5.25%, and 1.98% related to CNN, DeepRice, and FasterR-CNN, respectively.