Search Results (32)

Strawberry is a globally important yet stress-sensitive crop, increasingly threatened by combined abiotic and biotic stresses. Unlike single stresses, combined stresses elicit unique, non-additive responses through complex signaling and gene regulatory networks. This review synthesizes current knowledge on the molecular mechanisms underlying strawberry responses to combined stresses, focusing on signal perception and transduction as well as gene regulation. We examine how combined stresses are perceived by membrane-localized sensors and calcium channels, and how these signals are transduced through MAPK (mitogen-activated protein kinase) cascades, CDPKs (calcium-dependent protein kinases), and hormonal crosstalk involving ABA (abscisic acid), JA (jasmonic acid), and ethylene. At the gene regulation level, we discuss the roles of key transcription factors (WRKY, NAC (NAM, ATAF1, ATAF2 and CUC2), GRAS (GAI-RGA-and-SCR), DREB (Dehydration-Responsive Element-Binding protein), bZIP (basic leucine zipper transcription factor), CAMTA (calmodulin-binding transcription activator), ARF (auxin response factor), and LAV (Leafy Cotyledon2–Abscisic Acid Insensitive3–Val)), transcriptional cascades, epigenetic regulation via DNA methylation, and post-transcriptional (miRNAs such as Fan-miR73) and post-translational (ubiquitination and phosphorylation) control mechanisms. The review also evaluates emerging mitigation strategies informed by these molecular insights, including genomic selection, and explores future directions such as CRISPR (clustered regularly interspaced short palindromic repeats)-based genome editing and multi-omics integration. We conclude that understanding the integrated signaling and gene regulatory networks is essential for developing climate-resilient strawberry cultivars capable of withstanding increasingly complex stress combinations. Full article

Negative plant–soil feedback (NPSF) drives yield decline in monocropping systems, yet how intraspecific competition modulates NPSF across planting densities remains unclear. We conducted a two-stage plant–soil feedback experiment using five crops (Triticum aestivum L., Zea mays L., Solanum lycopersicum L., Cucumis sativus L., and Panax notoginseng (Burkill) F.H. Chen) with contrasting NPSF intensities under four planting densities (30 × 30 to 8 × 8 cm). Crops with stronger NPSF (P. notoginseng) showed pronounced density-dependent biomass reductions, whereas those with moderate (S. lycopersicum, C. sativus) or low (Z. mays, T. aestivum) NPSF were largely density-insensitive. Given its sensitivity, P. notoginseng was used to explore mechanisms. High-density planting (8 × 8 cm) intensified NPSF, reducing seedling survival by 88.54% and biomass by 56.08% compared with low-density controls (30 × 30 cm). Microbiome profiling showed enrichment of pathogenic Fusarium spp. and depletion of beneficial Humicola spp. under high density. Metabolomic analysis identified linoleic acid and oleamide as key root exudates upregulated under high-density stress, which selectively stimulated Fusarium growth as preferred carbon sources. Collectively, these results reveal a density-dependent feedback in which intensified competition reshapes root exudation, promotes pathogen proliferation, and suppresses beneficial taxa, thereby amplifying NPSF. This provides mechanistic insights into microbially mediated NPSF under density stress and highlights the importance of optimizing planting density to sustain crop productivity. Full article

High-resolution remote sensing (HRRS) imagery enables the extraction of cropland information with high levels of detail, especially when combined with the impressive performance of deep convolutional neural networks (DCNNs) in understanding these images. Comprehending the factors influencing DCNNs’ performance in HRRS cropland extraction is of considerable importance for practical agricultural monitoring applications. This study investigates the impact of classifier selection and different training data characteristics on the HRRS cropland classification outcomes. Specifically, Gaofen-1 composite images with 2 m spatial resolution are employed for HRRS cropland extraction, and two county-wide regions with distinct agricultural landscapes in Shandong Province, China, are selected as the study areas. The performance of two deep learning (DL) algorithms (UNet and DeepLabv3+) and a traditional classification algorithm, Object-Based Image Analysis with Random Forest (OBIA-RF), is compared. Additionally, the effects of different band combinations, crop growth stages, and class mislabeling on the classification accuracy are evaluated. The results demonstrated that the UNet and DeepLabv3+ models outperformed OBIA-RF in both simple and complex agricultural landscapes, and were insensitive to the changes in band combinations, indicating their ability to learn abstract features and contextual semantic information for HRRS cropland extraction. Moreover, compared with the DL models, OBIA-RF was more sensitive to changes in the temporal characteristics. The performance of all three models was unaffected when the mislabeling error ratio remained below 5%. Beyond this threshold, the performance of all models decreased, with UNet and DeepLabv3+ showing similar performance decline trends and OBIA-RF suffering a more drastic reduction. Furthermore, the DL models exhibited relatively low sensitivity to the patch size of sample blocks and data augmentation. These findings can facilitate the design of operational implementations for practical applications. Full article

According to Victor Ernest Shelford’s ‘Law of Tolerance,’ organisms within ecosystems thrive optimally when environmental conditions are favorable. Applying this principle to ecosystems and agro-ecosystems facing water scarcity or environmental challenges can significantly enhance their productivity. In these ecosystems, phytocenosis adjusts its conditions by utilizing water with varying salinity levels. Moreover, establishing optimal drinking water conditions for human populations within an ecosystem can help mitigate future negative succession processes. The purpose of this study is to evaluate the quality of two distinct water sources in the Amudarya district of the Republic of Karakalpakstan, Uzbekistan: collector-drainage water and groundwater at depths of 10 to 25 m. This research is highly relevant in the context of climate change, as improper management of water salinity, particularly in collector-drainage water, may exacerbate soil salinization and degrade drinking water quality. The primary methodology of this study is as follows: The Food and Agriculture Organization of the United Nations (FAO) standard for collector-drainage water is applied, and the water quality index is assessed using the CCME WQI model. The Canadian Council of Ministers of the Environment (CCME) model is adapted to assess groundwater quality using Uzbekistan’s national drinking water quality standards. The results of two years of collected data, i.e., 2021 and 2023, show that the water quality index of collector-drainage water indicates that it has limited potential for use as secondary water for the irrigation of sensitive crops and has been classified as ‘Poor’. As a result, salinity increased by 8.33% by 2023. In contrast, groundwater quality was rated as ‘Fair’ in 2021, showing a slight deterioration by 2023. Moreover, a comparative analysis of CCME WQI values for collector-drainage and groundwater in the region, in conjunction with findings from Ethiopia, India, Iraq, and Turkey, indicates a consistent decline in water quality, primarily due to agriculture and various other anthropogenic pollution sources, underscoring the critical need for sustainable water resource management. This study highlights the need to use organic fertilizers in agriculture to protect drinking water quality, improve crop yields, and promote soil health, while reducing reliance on chemical inputs. Furthermore, adopting WQI models under changing climatic conditions can improve agricultural productivity, enhance groundwater quality, and provide better environmental monitoring systems. Full article

Drought is a serious environmental challenge that reduces the productivity of valuable crops, including wheat. Brassinosteroids (BRs) is a group of phytohormones that have been used to enhance wheat drought tolerance. Wheat cultivars with different adaptation strategies could have their own specific drought tolerance mechanisms, and could react differently to treatment with growth regulators. In this work, the effect of seed pretreatment with 0.4 µM 24-epibrassinolide (EBR) was investigated in two wheat (Triticum aestivum L.) cultivars contrasting in drought behavior, tolerant Ekada 70 (cv. E70) and sensitive Zauralskaya Zhemchuzhina (cv. ZZh), in early ontogenesis under dehydration (PEG-6000) or soil drought conditions. EBR pretreatment mitigated the stress-induced inhibition of seedling emergence and growth, as well as membrane damage in cv.E70 but not in ZZh. An enzyme-linked immunosorbent assay (ELISA) revealed substantial changes in hormonal balance associated with ABA accumulation and a drop in the levels of IAA and cytokinins (CKs) in drought-subjected seedlings of both cultivars, especially ZZh. EBR-pretreatment reduced drought-induced hormone imbalance in cv. E70, while it did not have the same effect on ZZh. EBR-induced changes in the content of wheat germ agglutinin (WGA) belonging to the protective proteins in E70 seedlings suggest its contribution to EBR-dependent adaptive responses. The absence of a detectable protective effect of EBR on the ZZh cultivar may be associated with its insensitivity to pre-sowing EBR treatment. Full article

Establishing an optimal population structure is the fundamental approach to achieving high crop yield. By studying the changes in spring wheat yield and population structure under varying degrees of drought–rehydration conditions under drip irrigation, we can understand the balance between growth and stress response, explore the potential of wheat for biological water saving, and provide scientific evidence for the efficient production of drip-irrigated wheat in drought-prone areas. In this study, we used “Xinchun 6” (water-insensitive variety, XC 6) and “Xinchun 22” (water-sensitive variety, XC 22) as materials. Under two-year field planting conditions, mild (T1, J1, 60~65% FC, FC represents field capacity) and moderate (T2, J2, 45~50% FC) drought stress treatments were applied during the tillering and jointing stages, followed by drip irrigation for rehydration. The conventional drip irrigation served as the control (CK, 75~80% FC). We analyzed the relationship between the population quality and yield of different genotypes of wheat under water stress during the growth period and clarified the response of dry matter translocation to grains and high-quality populations to drought–rehydration. The results showed that drought stress reduced the tiller number (NT), leaf area index (LAI), grain number–leaf ratio (GNL), grain weight–leaf ratio (GWL), and dry matter weight. After rehydration, LAI, specific leaf weight (SLW), GNL, GWL, dry matter of vegetative organ and grain weight, and grain yield all reached their maximum values under T1 treatment. Compared with CK and moderate drought treatments (T2 and J2, respectively), these indicators under T1 treatment increased by an average of 1.04~30.96%, 0.82~6.28%, 0.57~26.10%, 0.41~8.01%, 0.48~41.10%, 0.53~13.97%, and 0.17~49.75%, respectively. Additionally, T1 treatment improved the post-flowering dry matter translocation rate and contribution rate. The compensatory effects on NT, LAI, GNL, GWL, and yield under drought–rehydration treatments during the tillering stage (T1 and T2) were superior to those during the jointing stage (J1 and J2). Correlation and path analysis indicated that yield was significantly positively correlated with LAI, GNL, and GWL, and increasing LAI had the best effect on yield increase. This suggests that rehydration after mild drought stress (T1) during the tillering stage can maintain a suitable leaf area for the population, enhance the grain–leaf ratio, promote post-anthesis material production and storage material transportation, coordinate the source–sink relationship, and achieve high yields for drip-irrigated spring wheat. Full article

Accurately understanding the critical threshold of leaf magnesium (Mg) concentration is crucial for rapid diagnosis of crop Mg status; however, little information is available on critical Mg concentration for different physiological processes in dicots and monocots. Here, we investigated the sensitivity of biomass, chlorophyll (Chl) at different leaf positions/ages, leaf area (LA), and photosynthesis (P_n) to Mg deficiency between rice (Oryza sativa L.) and cucumber (Cucumis sativus L.). Plants were grown hydroponically under twelve Mg concentration gradients. Results showed reducing the external Mg supply to a certain level resulted in significant decline in biomass, Chl, LA, and P_n in both plants. A leaf Mg threshold of 0.97 mg g⁻¹ DM (dry matter) for total biomass was found in rice, which was not identified in cucumber. Critical Mg thresholds for Chl a, b, and carotenoids (Car) showed a decreasing trend with leaf age, suggesting Chl in upper young leaves are more sensitive to Mg deficiency; however, visible Mg-deficiency symptoms were predominantly in mid-aged leaves with a higher rate of Mg remobilization, especially in cucumber. Leaf critical Mg concentrations for Chl a+b, P_n, and LA were 1.22, 1.05, and 1.00 mg g⁻¹ DM in rice, respectively, which were lower than those of cucumber, 4.23, 4.09, and 3.55 mg g⁻¹ DM, implying that cucumber was more susceptible to low Mg stress; Chl a+b was the most sensitive indicator of Mg deficiency. Overall, Chl a+b of upper young mature leaves can be used as an early diagnostic index of Mg nutrition in crops, especially Mg-insensitive crops. Full article

Maize is a globally important cereal and fodder crop. Accurate monitoring of maize planting densities is vital for informed decision-making by agricultural managers. Compared to traditional manual methods for collecting crop trait parameters, approaches using unmanned aerial vehicle (UAV) remote sensing can enhance the efficiency, minimize personnel costs and biases, and, more importantly, rapidly provide density maps of maize fields. This study involved the following steps: (1) Two UAV remote sensing-based methods were developed for monitoring maize planting densities. These methods are based on (a) ultrahigh-definition imagery combined with object detection (UHDI-OD) and (b) multispectral remote sensing combined with machine learning (Multi-ML) for the monitoring of maize planting densities. (2) The maize planting density measurements, UAV ultrahigh-definition imagery, and multispectral imagery collection were implemented at a maize breeding trial site. Experimental testing and validation were conducted using the proposed maize planting density monitoring methods. (3) An in-depth analysis of the applicability and limitations of both methods was conducted to explore the advantages and disadvantages of the two estimation models. The study revealed the following findings: (1) UHDI-OD can provide highly accurate estimation results for maize densities (R² = 0.99, RMSE = 0.09 plants/m²). (2) Multi-ML provides accurate maize density estimation results by combining remote sensing vegetation indices (VIs) and gray-level co-occurrence matrix (GLCM) texture features (R² = 0.76, RMSE = 0.67 plants/m²). (3) UHDI-OD exhibits a high sensitivity to image resolution, making it unsuitable for use with UAV remote sensing images with pixel sizes greater than 2 cm. In contrast, Multi-ML is insensitive to image resolution and the model accuracy gradually decreases as the resolution decreases. Full article

Paw San Hmwe (PSH) is an indigenous rice variety from Myanmar with a good taste, a pleasant fragrance, and excellent elongation ability during cooking. However, its low yield potential and strong photoperiod sensitivity reduce its productivity, and it is vulnerable to climate changes during growth. To improve the photoperiod insensitivity, yield, and plant stature of PSH, the high-yield genes Grain number 1a (Gn1a) and Wealthy Farmer’s Panicle (WFP), together with the photoperiod insensitivity trait, were introgressed into PSH via marker-assisted backcross breeding and phenotype selection. For the photoperiod insensitivity trait, phenotypic selection was performed under long-day conditions during the dry season. After foreground selection of Gn1a and WFP via simple sequence repeat genotyping, genotyping-by-sequencing was conducted to validate the introgression of target genes and determine the recurrent parent genome recovery of the selected lines. The improved lines were insensitive to photoperiod, and the Gn1a and WFP introgression lines showed significantly higher numbers of primary panicle branches and spikelets per panicle than the recurrent parent, with comparative similarity in cooking and eating qualities. This study successfully improved PSH by decreasing its photoperiod sensitivity and introducing high-yield genes via marker-assisted selection. The developed lines can be used for crop rotation and double-season cropping of better-quality rice. Full article

The potato is the third largest food crop, and nitrogen fertilizer is important for increasing potato yields; however, the shallow root system of potatoes causes the nitrogen fertilizer utilization rate to be low, which results in waste and environmental pollution, meaning that high nitrogen efficiency breeding is highly significant for potatoes. In the high nitrogen efficiency breeding of potatoes, genes with a nitrogen-deficient response should first be identified, and RNA-seq is an efficient method for identifying nitrogen-deficiency-response genes. In this study, two potato cultivars, Dongnong 322 (DN322) and Dongnong 314 (DN314), were utilized, and two nitrogen fertilizer application rates (N0 and N1) were set for both cultivars. Through the determination of physiological indicators, we identified that DN314 is more sensitive to nitrogen fertilizer, while DN322 is relatively insensitive to nitrogen fertilizer. Samples were taken at the seedling and tuber formation stage. At the seedling stage, DN322 and DN314 had 573 and 150 differentially expressed genes (DEGs), while at the tuber formation stage, they had 59 and 1905 DEGs, respectively. A total of three genes related to a low-nitrogen response were obtained via the combined analysis of differentially expressed genes (DEGs) and weighted correlation network analysis (WGCNA), of which two genes were obtained at the tuber formation stage and one gene in the seedling stage, providing theoretical guidance for the high nitrogen efficiency breeding of potatoes. Full article

Accurate estimation of canopy chlorophyll content (CCC) is critically important for agricultural production management. However, vegetation indices derived from canopy reflectance are influenced by canopy structure, which limits their application across species and seasonality. For horizontally homogenous canopies such as field crops, LAI and leaf inclination angle distribution or leaf mean tilt angle (MTA) are two biophysical characteristics determining canopy structure. Since CCC is relevant to LAI, MTA is the only structural parameter affecting the correlation between CCC and vegetation indices. To date, there are few vegetation indices designed to minimize MTA effects for CCC estimation. Herein, in this study, CCC-sensitive and MTA-insensitive satellite broadband vegetation indices are developed for crop canopy chlorophyll content estimation. The most efficient broadband vegetation indices for four satellite sensors (Sentinel-2, RapidEye, WorldView-2 and GaoFen-6) with red edge channels were identified (in the context of various vegetation index types) using simulated satellite broadband reflectance based on field measurements and validated with PROSAIL model simulations. The results indicate that developed vegetation indices present strong correlations with CCC and weak correlations with MTA, with overall R² of 0.76–0.80 and 0.84–0.95 for CCC and R² of 0.00 and 0.00–0.04 in the field measured data and model simulations, respectively. The best vegetation indices identified in this study are the soil-adjusted index type index SAI (B6, B7) for Sentinel-2, Verrelts’s three-band spectral index type index BSI-V (NIR1, Red, Red Edge) for WorldView-2, Tian’s three-band spectral index type index BSI-T (Red Edge, Green, NIR) for RapidEye and difference index type index DI (B6, B4) for GaoFen-6. The identified indices can potentially be used for crop CCC estimation across species and seasonality. However, real satellite datasets and more crop species need to be tested in further studies. Full article

Synthetic nitrogen (N) fertilizers and their increased production and utilization have played a great role in increasing crop yield and in meeting the food demands resulting from population growth. Nitrate (NO₃⁻) is the common form of nitrogen absorbed by plants. It has high water solubility and low retention by soil particles, making it prone to leaching and mobilization by surface water, which can seriously contaminate biological environments and affect human health. Few methods exist to measure nitrate in the soil. The development of ion selective sensors provides knowledge about the dynamics of nitrate in the soil in real time, which can be very useful for nitrate management. The objective of this study is to analyze the performance of three commercial probes (Nutrisens, RIKA and JXCT) under the same conditions. The performance was analyzed with respect to electrical conductivity (EC) (0–50 mS/cm) and nitrate concentration in aqueous solution and in sand (0–180 ppm NO₃⁻) at 35% volumetric soil moisture. Differences were shown among probes when studying their response to variations of the EC and, notably, only the Nutrisens probe provided coherent accurate measurements. In the evaluation of nitrate concentration in liquid solution, all probes proved to be highly sensitive. Finally, in the evaluation of all probes’ response to modifications in nitrate concentration in sand, the sensitivity decreased for all probes, with the Nutrisens probe the most sensitive and the other two probes almost insensitive. Full article

Pigeonpea is a photoperiod-sensitive crop; therefore, the introgression of photoperiod insensitivity could increase its adaptability to new environments. We determined the effect of extended daylength (ED; 16 h light) on the phenotypical traits of extra-early, early, and mid-early maturing pigeonpea introgression lines (ILs) derived from wild Cajanus species belonging to secondary and tertiary gene pools. Plants were grown under natural daylength and extended daylength in a greenhouse. Comparisons of the time of floral bud initiation, days to flowering, plant height, number of branches, and number of leaf nodes on the main stem at flowering revealed photoperiod-insensitive lines. All traits varied widely among the ILs. Analyses of flowering traits revealed large genetic components with low genotype × treatment interactions and high broad-sense heritability. The photoperiod most strongly affected the number of primary branches, followed by plant height. The extended day advanced flowering by approximately four days in extra-early ILs, confirming that these ILs are quantitative, short-day plants. The photoperiod insensitivity index varied from 0.88 in ICPP 171541 (moderately photoperiod sensitive) to 0.99 in ICPP 171546 and ICPP 171561 (photoperiod insensitive). These photoperiod-insensitive extra-early flowering ILs can be used to enrich the genetic diversity of pigeonpea and to develop photoperiod-insensitive cultivars for cultivation in new environments. Full article

The decrease in water resources due to climate change is expected to have a significant impact on agriculture. On the other hand, as the world population increases so does the demand for food. It is necessary to better manage environmental resources and maintain an adequate level of crop production in a world where the population is constantly increasing. Therefore, agricultural activities must be closely monitored, especially in maize fields since maize is of great importance to both humans and animals. Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical satellite images were used to monitor maize growth in this study. Backscatter and interferometric coherence values derived from Sentinel-1 images, as well as Normalized Difference Vegetation Index (NDVI) and values related to biophysical variables (such as Leaf Area Index (LAI), Fraction of Vegetation Cover (fCover or FVC), and Canopy Water Content (CW)) derived from Sentinel-2 images were investigated. Sentinel-1 images were also used to calculate plant heights. The Interferometric SAR (InSAR) technique was applied to calculate interferometric coherence values and plant heights. For the plant height calculation, two image pairs with the largest possible perpendicular baseline were selected. Backscatter, NDVI, LAI, fCover, and CW values were low before planting, while the interferometric coherence values were generally high. Backscatter, NDVI, LAI, fCover, and CW values increased as the maize grew, while the interferometric coherence values decreased. Among all Sentinel-derived values, fCover had the best correlation with maize height until maize height exceeded 260 cm (R² = 0.97). After harvest, a decrease in backscatter, NDVI, LAI, fCover, and CW values and an increase in interferometric coherence values were observed. NDVI, LAI, fCover, and CW values remained insensitive to tillage practices, whereas backscatter and interferometric coherence values were found to be sensitive to planting operations. In addition, backscatter values were also sensitive to irrigation operations, even when the average maize height was about 235 cm. Cloud cover and/or fog near the study area were found to affect NDVI, LAI, fCover, and CW values, while precipitation events had a significant impact on backscatter and interferometric coherence values. Furthermore, using Sentinel-1 images, the average plant height was calculated with an error of about 50 cm. Full article

Cucumber (Cucumis sativus L.) is one of the most popular cultivated vegetable crops but it is intrinsically sensitive to cold stress due to its thermophilic nature. To explore the molecular mechanism of plant response to low temperature (LT) and the mitigation effect of exogenous nitric oxide (NO) on LT stress in cucumber, transcriptome changes in cucumber leaves were compared. The results showed that LT stress regulated the transcript level of genes related to the cell cycle, photosynthesis, flavonoid accumulation, lignin synthesis, active gibberellin (GA), phenylalanine metabolism, phytohormone ethylene and salicylic acid (SA) signaling in cucumber seedlings. Exogenous NO improved the LT tolerance of cucumber as reflected by increased maximum photochemical efficiency (Fv/Fm) and decreased chilling damage index (CI), electrolyte leakage and malondialdehyde (MDA) content, and altered transcript levels of genes related to phenylalanine metabolism, lignin synthesis, plant hormone (SA and ethylene) signal transduction, and cell cycle. In addition, we found four differentially expressed transcription factors (MYB63, WRKY21, HD-ZIP, and b-ZIP) and their target genes such as the light-harvesting complex I chlorophyll a/b binding protein 1 gene (LHCA1), light-harvesting complex II chlorophyll a/b binding protein 1, 3, and 5 genes (LHCB1, LHCB3, and LHCB5), chalcone synthase gene (CSH), ethylene-insensitive protein 3 gene (EIN3), peroxidase, phenylalanine ammonia-lyase gene (PAL), DNA replication licensing factor gene (MCM5 and MCM6), gibberellin 3 beta-dioxygenase gene (GA3ox), and regulatory protein gene (NPRI), which are potentially associated with plant responses to NO and LT stress. Notably, HD-ZIP and b-ZIP specifically responded to exogenous NO under LT stress. Taken together, these results demonstrate that cucumber seedlings respond to LT stress and exogenous NO by modulating the transcription of some key transcription factors and their downstream genes, thereby regulating photosynthesis, lignin synthesis, plant hormone signal transduction, phenylalanine metabolism, cell cycle, and GA synthesis. Our study unveiled potential molecular mechanisms of plant response to LT stress and indicated the possibility of NO application in cucumber production under LT stress, particularly in winter and early spring. Full article