Search Results (1,179)

In China’s Loess Plateau, sustainable agricultural end products are affected by an insufficiency of water resources. Rising crop water use efficiency (WUE) through field management pattern improvement is a crucial plan of action to address this issue. However, there is no agreement among researchers on the most appropriate field management practices regarding WUE, which requires further integrated quantitative analysis. We conducted a meta-analysis by quantifying the effect of agricultural practices surrounding nitrogen (N) fertilizer management. The two experimental cultivars were Yunhan–20410 and Yunhan–618. The subplots included nitrogen 0 kg·ha⁻¹ (N0), 90 kg·ha⁻¹ (N90), 180 kg·ha⁻¹ (N180), 210 kg·ha⁻¹ (N210), and 240 kg·ha⁻¹ (N240). Our results show that higher N rates (up to N210) enhanced water consumption during the node-flowering and flowering-maturity time periods. YH–618 showed higher water use during the sowing–greening and node-flowering periods but decreased use during the greening-node and flowering-maturity periods compared to YH–20410. The N210 treatment under YH–618 maximized water use efficiency (WUE). Increased N rates (N180–N210) decreased covering temperatures (T_max, T_min, T_aver) during flowering, increasing the level of grain filling. Spike numbers rose with N application, with an off-peak at N210 for strong-gluten wheat. The 1000-grain weight was at first enhanced but decreased at the far end of N180–N210. YH–618 with N210 achieved a harvest index (HI) similar to that of YH–20410 with N180, while excessive N (N240) or water reduced the HI. Dry matter accumulation increased up to N210, resulting in earlier stabilization. Soil water consumption from wintering to jointing was strongly correlated with pre-flowering dry matter biological process and yield, while jointing–flowering water use was linked to post-flowering dry matter and spike numbers. Post-flowering dry matter accumulation was critical for yield, whereas spike numbers positively impacted yield but negatively affected 1000-grain weight. In conclusion, our results provide evidence for determining suitable integrated agricultural establishment strategies to ensure efficient water use and sustainable production in the Loess Plateau region. Full article

Contact unmodified antisense DNA biotechnology (CUADb), developed in 2008, employs short antisense DNA oligonucleotides (oligos) as a novel approach to insect pest control. These oligonucleotide-based insecticides target pest mature rRNAs and/or pre-rRNAs and have demonstrated high insecticidal efficacy, particularly against sap-feeding insect pests, which are key vectors of plant DNA viruses and among the most economically damaging herbivorous insects. To further explore the potential of CUADb, this study evaluated the insecticidal efficacy of short 11-mer antisense DNA oligos against Coccus hesperidum, in comparison with long 56-mer single-stranded and double-stranded DNA sequences. The short oligos exhibited higher insecticidal activity. By day 9, the highest mortality rate (97.66 ± 4.04%) was recorded in the Coccus-11 group, while the most effective long sequence was the double-stranded DNA in the dsCoccus-56 group (77.09 ± 6.24%). This study also describes the architecture of the DNA containment (DNAc) mechanism, highlighting the intricate interactions between rRNAs and various types of DNA oligos. During DNAc, the Coccus-11 treatment induced enhanced ribosome biogenesis and ATP production through a metabolic shift from carbohydrates to lipid-based energy synthesis. However, this ultimately led to a ‘kinase disaster’ due to widespread kinase downregulation resulting from insufficient ATP levels. All DNA oligos with high or moderate complementarity to target rRNA initiated hypercompensation, but subsequent substantial rRNA degradation and insect mortality occurred only when the oligo sequence perfectly matched the rRNA. Both short and long oligonucleotide insecticide treatments led to a 3.75–4.25-fold decrease in rRNA levels following hypercompensation, which was likely mediated by a DNA-guided rRNase, such as RNase H1, while crucial enzymes of RNAi (DICER1, Argonaute 2, and DROSHA) were downregulated, indicating fundamental difference in molecular mechanisms of DNAc and RNAi. Consistently, significant upregulation of RNase H1 was detected in the Coccus-11 treatment group. In contrast, treatment with random DNA oligos resulted in only a 2–3-fold rRNA decrease, consistent with the normal rRNA half-life maintained by general ribonucleases. These findings reveal a fundamental new mechanism of rRNA regulation via complementary binding between exogenous unmodified antisense DNA and cellular rRNA. From a practical perspective, this minimalist approach, applying short antisense DNA dissolved in water, offers an effective, eco-friendly and innovative solution for managing sternorrhynchans and other insect pests. The results introduce a promising new concept in crop protection: DNA-programmable insect pest control. Full article

Tea cultivation faces significant threats from red spider mite (RSM: Oligonychus coffeae) infestations, which reduce yields and economic viability in major tea-producing regions. Current automated detection methods rely on supervised deep learning models requiring extensive labeled data, limiting scalability for smallholder farmers. This article proposes AIM-Net (AI-based Infestation Mapping Network) by evaluating SwAV (Swapping Assignments between Views), a self-supervised learning framework, for classifying RSM infestation severity (Mild, Moderate, Severe) using a geo-referenced, field-acquired dataset of RSM infested tea-leaves, Cam-RSM. The methodology combines SwAV pre-training on unlabeled data with fine-tuning on labeled subsets, employing multi-crop augmentation and online clustering to learn discriminative features without full supervision. Comparative analysis against a fully supervised ResNet-50 baseline utilized 5-fold cross-validation, assessing accuracy, F1-scores, and computational efficiency. Results demonstrate SwAV’s superiority, achieving 98.7% overall accuracy (vs. 92.1% for ResNet-50) and macro-average F1-scores of 98.3% across classes, with a 62% reduction in labeled data requirements. The model showed particular strength in Mild_RSM-class detection (F1-score: 98.5%) and computational efficiency, enabling deployment on edge devices. Statistical validation confirmed significant improvements (p < 0.001) over baseline approaches. These findings establish self-supervised learning as a transformative tool for precision pest management, offering resource-efficient solutions for early infestation detection while maintaining high accuracy. Full article

In the era of smart agriculture, accurate identification of plant species is critical for effective crop management, biodiversity monitoring, and the sustainable use of medicinal resources. However, existing deep learning approaches often underperform when applied to fine-grained plant classification tasks due to the lack of domain-specific, high-quality datasets and the limited representational capacity of traditional architectures. This study addresses these challenges by introducing a novel, well-curated leaf image dataset consisting of 39 classes of medicinal and aromatic plants collected from the Aromatic and Medicinal Plant Research Station in Odakkali, Kerala, India. To overcome performance bottlenecks observed with a baseline Convolutional Neural Network (CNN) that achieved only 44.94% accuracy, we progressively enhanced model performance through a series of architectural innovations. These included the use of a pre-trained VGG16 network, data augmentation techniques, and fine-tuning of deeper convolutional layers, followed by the integration of Squeeze-and-Excitation (SE) attention blocks. Ultimately, we propose a hybrid deep learning architecture that combines VGG16 with Batch Normalization, Gated Recurrent Units (GRUs), Transformer modules, and Dilated Convolutions. This final model achieved a peak validation accuracy of 95.24%, significantly outperforming several baseline models, such as custom CNN (44.94%), VGG-19 (59.49%), VGG-16 before augmentation (71.52%), Xception (85.44%), Inception v3 (87.97%), VGG-16 after data augumentation (89.24%), VGG-16 after fine-tuning (90.51%), MobileNetV2 (93.67), and VGG16 with SE block (94.94%). These results demonstrate superior capability in capturing both local textures and global morphological features. The proposed solution not only advances the state of the art in plant classification but also contributes a valuable dataset to the research community. Its real-world applicability spans field-based plant identification, biodiversity conservation, and precision agriculture, offering a scalable tool for automated plant recognition in complex ecological and agricultural environments. Full article

The combined effect of non-thermal plasma (NTP) and hydropriming on the germination performance and seedling characteristics of specific varieties of cucumber (Cucumis sativus L.) and broccoli (Brassica oleracea var. italica Plenck.) seeds after short-term storage is reported. Seeds were treated with NTP for 10 and 15 min, followed by hydropriming in distilled water for 24 h, and then stored for six months in the dark before evaluation. The treated cucumber seeds demonstrated a statistically significant enhancement in seed germination and seedling vitality indices. In contrast, broccoli seeds showed no significant improvement. The stimulatory effects observed in cucumber may be attributed to reactive oxygen and nitrogen species, which act as signaling molecules to promote stress tolerance and early growth. This study also highlights the potential of combined NTP treatment and hydropriming as a pre-sowing treatment for select crops, underscoring the need for species-specific optimization. The used, portable, and relatively inexpensive NTP device offers practical advantages for agricultural applications. Full article

Within the framework of organic acid alternatives to chemical herbicides, pre-emergence weed control research is scarce. Citric acid (CA) and lactic acid (LA), considered significantly less effective than pelargonic acid (PA) and acetic acid (AA) from post-emergence (foliar spraying) studies, have largely been disregarded. This in vitro study was aimed at comparing the effects of 5–20% AA, AA + essential oils, PA, CA, and LA on radicle emergence inhibition (direct spraying of seeds) and shoot emergence inhibition (application to peat) on both weeds (perennial ryegrass, green foxtail, common vetch and chicory) and crops (soft wheat, alfalfa and millet). All tested compounds demonstrated concentration-dependent and species-specific effects on shoot emergence inhibition, with CA and LA (IC₅₀ range: 3.4–19.3%) showing a comparable efficacy to PA and AA (IC₅₀ range: 3.1–35.9%). The results also showed that CA and, to a lesser extent, LA were less inhibitory to soft wheat (CA IC₅₀ = 62.5%; LA IC₅₀ = 35.9%) and alfalfa (CA IC₅₀ = 57.8%; LA IC₅₀ = 44.1%) shoot emergence. CA and LA show potential promise for pre-emergence weed control in field testing, either on a stale seedbed in pre-crop sowing or concurrently with soft wheat and alfalfa sowing. Investigating organic compound herbicidal effects on crops of interest warrants attention. Full article

Breeding programs often overlook the use of root traits. Therefore, we investigated the relevance of sorghum root traits in explaining its adaptation to combined drought and heat stress (CDHS). Six (i.e., three pre-release lines + three checks) sorghum genotypes were established at two low-altitude (i.e., <600 masl) locations with a long-term history of averagely very high temperatures in the beginning of the summer season, under two management (i.e., CDHS and well-watered (WW)) regimes. At each location, the genotypes were laid out in the field using a randomized complete block design (RCBD) replicated two times. Root trait data, namely root diameter (RD), number of roots (NR), number of root tips (NRT), total root length (TRL), root depth (RDP), root width (RW), width–depth ratio (WDR), root network area (RNA), root solidity (RS), lower root area (LRA), root perimeter (RP), root volume (RV), surface area (SA), root holes (RH) and root angle (RA) were gathered using the RhizoVision Explorer software during the pre- and post-flowering stage of growth. RSA traits differentially showed significant (p < 0.05) correlations with grain yield (GY) at pre- and post-flowering growth stages and under CDHS and WW conditions also revealing genotypic variation estimates exceeding 50% for all the traits. Regression models varied between pre-flowering (p = 0.013, R² = 47.15%, R² Predicted = 29.32%) and post-flowering (p = 0.000, R² = 85.64%, R² Predicted = 73.30%) growth stages, indicating post-flowering as the optimal stage to relate root traits to yield performance. RD contributed most to the regression model at post-flowering, explaining 51.79% of the 85.64% total variation. The Smith–Hazel index identified ICSV111IN and ASAREACA12-3-1 as superior pre-release lines, suitable for commercialization as new varieties. The study demonstrated that root traits (in particular, RD, RW, and RP) are linked to crop performance under CDHS conditions and should be incorporated in breeding programs. This approach may accelerate genetic gains not only in sorghum breeding programs, but for other crops, while offering a nature-based breeding strategy for stress adaptation in crops. Full article

Nowadays, there is special interest in promoting the consumption of ancestral crops and minimally processed foods with high nutritional value. However, besides nutritional issues, safety assessments must be addressed. This study aimed to evaluate mycotoxin contamination in five minimally processed traditional Ecuadorian foods: ochratoxin A (OTA), fumonisin B₁ (FB₁), and aflatoxins (AFs) in brown rice, lupin, and quinoa; OTA, FB₁, and deoxynivalenol (DON) in whole-wheat flour; and OTA and AFs in peanuts. Samples (45 samples of peanuts and whole-wheat flour, 47 of brown rice, 46 of quinoa, and 36 of lupin) were collected from local markets and supermarkets in the three most populated cities in Ecuador. Mycotoxins were determined by RP-HPLC with fluorescence and detection. Results were compared with the maximum permitted levels (MPLs) of European Regulation 2023/915/EC. Overall contamination reached up to 59.8% of the analyzed samples (38.4% with one mycotoxin and 21.5% with co-occurrence). OTA was the most prevalent mycotoxin (in 82.6% of quinoa, 76.7% of whole-wheat flour, 53.3% of peanuts, 48.6% of lupin, and 25.5% of brown rice), and a modest number of quinoa (17%) and lupin (5.7%) samples surpassed the MPLs. DON was found in 82.2% of whole-wheat flour (28.9% > MPL). FB₁ was detected in above 25% of brown rice and whole-wheat flour and in 9% of the quinoa samples. FB₁ levels were above the MPLs only for whole-wheat flour (17.8%). AFB₁ and AFG₁ showed similar prevalence (about 6.5 and 8.5%, respectively) in quinoa and rice and about 27% in peanuts. Overall, these findings underscore the importance of enhancing fungal control in the pre- and post-harvest stages of these foods, which are recognized for their high nutritional value and ancestral worth; consequently, the results present key issues related to healthy diet promotion and food sovereignty. This study provides compelling insights into mycotoxin occurrence in minimally processed Ecuadorian foods and highlights the need for further exposure assessments by combining population consumption data. Full article

Carbon nanotubes are promising in agriculture for improving crop resilience and delivering agrochemicals. However, their effects on insect pests, especially chewing pests such as the fall armyworm (Spodoptera frugiperda), remain underexplored. In this study, we investigated how two types of functionalized multi-walled carbon nanotubes—hydroxylated (MWCNTs-OH) and carboxylated (MWCNTs-COOH), both obtained from Jiangsu Xianfeng Nano (Nanjing, China)—affect the pest’s development and reproduction. Using an age-stage two-sex life table approach, we fed larvae diets containing 0.04, 0.4, or 4 mg/g of these nanomaterials. Both types of MWCNTs exhibited concentration-dependent inhibitory effects. At the highest dose (4 mg/g), larval development was significantly prolonged, adult pre-oviposition periods increased, and fecundity (egg production) sharply declined, especially with MWCNTs-OH. Population growth parameters were also suppressed: net reproductive rate (R₀), intrinsic rate of increase (r), and finite rate of increase (λ) were reduced at 4 mg/g, particularly with MWCNTs-OH, while mean generation time (T) was extended with MWCNTs-COOH. Overall, MWCNTs-OH demonstrated a greater inhibitory impact compared to MWCNTs-COOH. These findings suggest that functionalized MWCNTs could serve as potential novel pest control agents against S. frugiperda by impeding its development and reproduction. Full article

The floral transition in Chinese cabbage (Brassica rapa ssp. pekinensis) is governed by a complex interplay of gene expression and hormonal regulation. Temporal transcriptome profiling was conducted across three developmental stages: pre-bolting (PBS), bolting (BS), and flowering stages (FS), to investigate the underlying molecular mechanisms. A total of 7092 differentially expressed genes (DEGs) were identified, exhibiting distinct expression trajectories during the transition. Moreover, functional enrichment analyses revealed strong associations with plant hormone signaling, MAPK pathways, and developmental regulation processes. Key flowering-related genes, such as BrFLM, BrAP2, BrFD, BrFT, and BrSOC1s displayed antagonistic expression patterns. Hormonal pathways involving auxin, ABA, ET, BR, GA, JA, CK, and SA showed stage-dependent modulation. Further, orphan genes (OGs), especially EARLY FLOWERING 1 (EF1), showed significant upregulation during the transition, which exhibited 1.84-fold and 1.93-fold increases at BS and FS compared to PBS, respectively (p < 0.05). Functional validation through EF1 overexpression (EF1OE) in Arabidopsis consistently promoted early flowering. The expression levels of AtFT and AtSOC1 were significantly upregulated in EF1OE lines compared to wild-type (WT) plants. The findings contribute to understanding the coordinated genetic and hormonal events driving floral development in Chinese cabbage, suggesting EF1 as a candidate for bolting resistance breeding. This work also expands the existing regulatory framework through the successful integration of OGs into the complex floral induction system of Brassica crops. Full article

Accurate estimation of winter wheat yield under saline stress is crucial for addressing food security challenges and optimizing agricultural management in regional soils. This study proposed a method integrating Sentinel-2 data and field-measured soil salt content (SC) using a random forest (RF) method to improve yield estimation of winter wheat in Kenli County, a typical saline area in China’s Yellow River Delta. First, feature importance analysis of a temporal vegetation index (VI) and salinity index (SI) across all growth periods were achieved to select main parameters. Second, yield models of winter wheat were developed in VI-, SI-, VI + SI-, and VI + SI + SC-based groups. Furthermore, error assessment and spatial yield mapping were analyzed in detail. The results demonstrated that feature importance varied by growth periods. SI dominated in pre-jointing periods, while VI was better in the post-jointing phase. The VI + SI + SC-based model achieved better accuracy (R² = 0.78, RMSE = 720.16 kg/ha) than VI-based (R² = 0.71), SI-based (R² = 0.69), and VI + SI-based (R² = 0.77) models. Error analysis results suggested that the residuals were reduced as the input parameters increased, and the VI + SI + SC-based model showed a good consistency with the field-measured yields. The spatial distribution of winter wheat yield using the VI + SI + SC-based model showed significant differences, and average yields in no, slight, moderate, and severe salinity areas were 7945, 7258, 5217, and 4707 kg/ha, respectively. This study can provide a reference for winter wheat yield estimation and crop production improvement in saline regions. Full article

Soil salinization is increasingly affecting agricultural areas worldwide, reducing soil quality and crop yields. Surface salinization evidences present complex spectral features, increasing in depth with increasing salt concentrations. For this reason, low salinization detection provides a complex challenge to test the capabilities of new-generation hyperspectral satellites. The aim of this study is to test the capability of the new generation of hyperspectral satellites (EnMAP) in detecting early stages and low levels of topsoil salinization and to investigate the differences between laboratory and image spectra to take into account their influence on model performance. The area of study, the Grosseto plain, located in central Italy, presented heterogeneous salinity levels ($E{C}_{max}$= 11.7 dS/m, $E{C}_{mean}$= 0.99 dS/m). We investigated the salt-affected soil spectral behaviour with both laboratory-acquired spectra ($n_{obs}$= 60) and EnMAP-derived spectra ($n_{obs}$= 20). Both datasets were pre-processed with multiple data transformation algorithms and 2D correlograms, PLSR and the Random Forest regressor were tested to identify the best model for salinity detection. Two-dimensional correlograms resulted in an $R^2$ of 0.88 for laboratory data and 0.63 for EnMAP data. PLSR proved to have the worst performance. The Random Forest regressor proved its capability in detecting complex spectral features, with $R^2$ scores of 0.72 for laboratory data and 0.60 for EnMAP. The Random Forest model provides very satisfactory mapping capabilities when tested on the whole study area. The results highlight that the EnMAP-derived dataset produces similar results to those of ASD laboratory spectra, providing evidences regarding EnMAP’s predictive capability to detect early stages of topsoil salinization. Full article

Recent progress in geospatial foundation models (GFMs) has demonstrated strong generalization capabilities for remote sensing downstream tasks. However, existing GFMs still struggle with fine-grained cropland classification due to ambiguous field boundaries, insufficient and low-efficient temporal modeling, and limited cross-regional adaptability. In this paper, we propose CropSTS, a remote sensing foundation model designed with a decoupled temporal–spatial attention architecture, specifically tailored for the temporal dynamics of cropland remote sensing data. To efficiently pre-train the model under limited labeled data, we employ a hybrid framework combining joint-embedding predictive architecture with knowledge distillation from web-scale foundation models. Despite being trained on a small dataset and using a compact model, CropSTS achieves state-of-the-art performance on the PASTIS-R benchmark in terms of mIoU and F1-score. Our results validate that structural optimization for temporal encoding and cross-modal knowledge transfer constitute effective strategies for advancing GFM design in agricultural remote sensing. Full article

Peas possess significant nutritional properties due to their high protein levels, carbohydrates, fiber, and vitamins. Increased climate variability can lead to water stress in crops like peas. Therefore, priming plants through seed priming is a technique that has proven effective as a pre-conditioning method for plants to cope with more severe future stresses. Different doses and soaking times of ‘Santa Isabel’ pea seeds in NaCl and H₂O₂ were evaluated to enhance and promote germination. Two experiments were conducted under controlled conditions (average temperature 15.8 °C) through a completely randomized design with a 4 × 3 factorial arrangement, comprising 12 treatments in each trial. In the first trial, NaCl doses (0, 50, 100, or 150 mM) and the soaking time of the seeds in NaCl (12, 24, or 36 h) were examined. In the second trial, H₂O₂ doses (0, 20, 40, or 60 mM) were tested with the same imbibition times. The 50 mM NaCl dose at 24 h demonstrated the best values for germination rate index, mean germination time, germination rate (GR), and germination potential (GP). Seed imbibition for 24 h in NaCl, as well as in H₂O₂, is the ideal time to achieve the best GR and GP. The dry mass of leaf and stipule recorded the highest values with a 60 mM dose of H₂O₂ and 24 h of imbibition. An application of 150 mM NaCl resulted in the highest values of germinated seed dry mass, while causing lower dry mass in roots, stems, leaves, and stipules; however, it maintained similar total dry mass values. Full article

Field trials were carried out over two tobacco cropping seasons (2020 and 2021) to assess the effectiveness of soil (PRE-T) and post-transplant (POST-T (OT)) herbicides in a tobacco crop, depending on rainfall and the type of soil. The effectiveness of PRE-T and POST-T (OT) herbicides alternated according to the presence of weeds, treatments, the region, and years. Unpredictable meteorological conditions throughout the two study years likely influenced the control of weeds. An unusually moist May in 2020 with a precipitation of 29 mm in the first WA PRE-T before the emergence of weeds generated the leaching of the PRE-T herbicide from the surface of the soil, which was likely the most probable reason for the reduced effectiveness of PRE-T-applied herbicides (less than 77%) in comparison to the POST-T (OT) application treatment in 2020 in the Prilep region. Conversely, the restricted rainfall after PRE-T and POST-T (OT) application may have caused the unsatisfactory efficacy of both PRE-T and POST-T (OT) herbicide treatments in the Titov Veles region in 2021 (less than 78 and 80%, respectively) in comparison with 2020. Excessive rain immediately after PRE-T and POST-T (OT) application resulted in the injury of tobacco plants in the Prilep region in 2020 and 2021, which was between 8 and 25%, and 7 and 22%, respectively, after seven DAHAs across both treatments. The injuries caused by pendimethalin and metolachlor were more serious. The yields of tobacco after both PRE-T and POST-T treatment in each region typically reflect the overall effectiveness of weed control and the extent of tobacco crop injury. Full article