Search Results (504)

In the irrigation districts of Northern China, the flood resources utilization for deep storage irrigation, which is essentially characterized by active excessive irrigation, aims to have the potential to mitigate freshwater shortages, and long-term groundwater overexploitation. It is crucial to detect the effects of irrigation amounts on agricultural yield and the mechanisms under deep storage irrigation. A three-year field experiment (2020–2023) was conducted in the Guanzhong Plain, according to five soil wetting layer depths (RF: 0 cm; W1: control, 120 cm; W2: 140 cm; W3: 160 cm; W4: 180 cm) with soil saturation water content as the irrigation upper limit. Results exhibited that, compared to W1, the W2, W3, and W4 treatments led to the increased plant height, leaf area index, and dry matter accumulation. Meanwhile, the W2, W3, and W4 treatments improved kernel weight increment achieving maximum grain-filling rate (W_max), maximum grain-filling rate (G_max), and average grain-filling rate (G_ave), thereby enhancing the effective spikes (ES) and grain number per spike (GS), and thus increased wheat grain yield (GY). In relative to W1, the W2, W3, and W4 treatments increased the ES, GS, and GY by 11.89–19.81%, 8.61–14.36%, and 8.17–13.62% across the three years. Notably, no significant difference was observed in GS and GY between W3 and W4 treatments, but W4 treatment displayed significant decreases in ES by 3.04%, 3.06%, and 2.98% in the respective years. The application of a structural equation modeling (SEM) revealed that deep storage irrigation improved ES and GS by positively regulating W_max, G_max, and G_ave, thus significantly increasing GY. Overall, this study identified the optimal threshold (W3 treatment) to maximize wheat yields by optimizing both the vegetative growth and grain-filling dynamics. This study provides essential support for the feasibility assessment of deep storage irrigation before flood seasons, which is vital for the balance and coordination of food security and water security. Full article

Wheat is one of the important grain crops, and spike counting is crucial for predicting spike yield. However, in complex farmland environments, the wheat body scale has huge differences, its color is highly similar to the background, and wheat ears often overlap with each other, which makes wheat ear detection work face a lot of challenges. At the same time, the increasing demand for high accuracy and fast response in wheat spike detection has led to the need for models to be lightweight function with reduced the hardware costs. Therefore, this study proposes a lightweight wheat ear detection model, CML-RTDETR, for efficient and accurate detection of wheat ears in real complex farmland environments. In the model construction, the lightweight network CSPDarknet is firstly introduced as the backbone network of CML-RTDETR to enhance the feature extraction efficiency. In addition, the FM module is cleverly introduced to modify the bottleneck layer in the C2f component, and hybrid feature extraction is realized by spatial and frequency domain splicing to enhance the feature extraction capability of wheat to be tested in complex scenes. Secondly, to improve the model’s detection capability for targets of different scales, a multi-scale feature enhancement pyramid (MFEP) is designed, consisting of GHSDConv, for efficiently obtaining low-level detail information and CSPDWOK for constructing a multi-scale semantic fusion structure. Finally, channel pruning based on Layer-Adaptive Magnitude Pruning (LAMP) scoring is performed to reduce model parameters and runtime memory. The experimental results on the GWHD2021 dataset show that the ${\mathrm{AP}}_{50}$ of CML-RTDETR reaches 90.5%, which is an improvement of 1.2% compared to the baseline RTDETR-R18 model. Meanwhile, the parameters and GFLOPs have been decreased to 11.03 M and 37.8 G, respectively, resulting in a reduction of 42% and 34%, respectively. Finally, the real-time frame rate reaches 73 fps, significantly achieving parameter simplification and speed improvement. Full article

Durum wheat, the world’s second most cultivated wheat species, is a staple crop, critical for global food security, including in Ethiopia where it serves as a center of diversity. However, climate change and genetic erosion threaten its genetic resources, necessitating genomic studies to support conservation and breeding efforts. This study characterized genome-wide diversity, population structure (STRUCTURE, principal coordinate analysis (PCoA), neighbor-joining trees, analysis of molecular variance (AMOVA)), and selection signatures (F_ST, Hardy–Weinberg deviations) in Ethiopian durum wheat by analyzing 376 genotypes (148 accessions) using an Illumina Infinium 25K single nucleotide polymorphism (SNP) array. A set of 7842 high-quality SNPs enabled the assessments, comparing landraces with cultivars and breeding populations. Results revealed moderate genetic diversity (mean polymorphism information content (PIC) = 0.17; gene diversity = 0.20) and identified 26 loci under selection, associated with key traits like grain yield, stress tolerance, and disease resistance. AMOVA revealed 80.1% variation among accessions, with no significant differentiation by altitude, region, or spike density. Landraces formed distinct clusters, harboring unique alleles, while admixture suggested gene flow via informal seed exchange. The findings highlight Ethiopia’s rich durum wheat diversity, emphasizing landraces as reservoirs of adaptive alleles for breeding. This study provides genomic insights to guide conservation and the development of climate-resilient cultivars, supporting sustainable wheat production globally. Full article

Late sowing and spring low temperatures have a great impact on the growth and maturation of wheat in the rice–wheat rotation region. In order to analyze the impacts of cold stress in February in early spring on yield formation and agronomic traits of wheat on different sowing dates, a controlled pot experiment was performed using the widely promoted and applied spring-type wheat variety Yangmai23 (YM23). The yield of wheat treated with late sowing date II (SDII, 21 November) and overly late sowing date III (SDIII, 9 December) were both lower than that of wheat sown on the suitable date I (SDI, 1 November). The yield of late-sown wheat decreased by 40.82% for SDII and by 66.77% for SDIII, compared with SDI, and these three treatments of wheat all grew under the natural conditions as the control treatments. The plant height, stem diameter of the internode below the ear, flag leaf length and area, and total awn length of the spike, as well as the spike length of late-sown wheat, were all significantly lower than those of wheat in SDI treatment. Early spring low temperatures exacerbated the decline in yield of wheat sown on different dates, to some extent. Despite showing higher net photosynthetic rate, stomatal conductance, and transpiration rate in flag leaves of the SDIII treatment under low-temperature stress than those of the other treatments at anthesis, overly late sowing led to minimal leaf area, shorter plant height, fewer tillers, and smaller ears, ultimately resulting in the lowest yield. Our study suggested that additional focus and some regulation techniques are needed to be studied further to mitigate the combined negative impacts of late sowing and low-temperature stress in early spring on wheat production. Full article

Accurate estimation aboveground biomass (AGB) in winter wheat is crucial for yield assessment but remains challenging to achieve non-destructively. Unmanned aerial vehicle (UAV)-based remote sensing offers a promising solution at the plot level. Traditional field sampling methods, such as random plant selection or full-quadrat harvesting, are labor intensive and may introduce substantial errors compared to the canopy-level estimates obtained from UAV imagery. This study proposes a novel method using Fractional Vegetation Coverage (FVC) to adjust field-sampled AGB to per-plant biomass, enhancing the accuracy of AGB estimation using UAV imagery. Correlation analysis and Variance Inflation Factor (VIF) were employed for feature selection, and estimation models for leaf, spike, stem, and total AGB were constructed using Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN) models. The aim was to evaluate the performance of multimodal data in estimating winter wheat leaves, spikes, stems, and total AGB. Results demonstrated that (1) FVC-adjusted per-plant biomass significantly improved correlations with most indicators, particularly during the filling stage, when the correlation between leaf biomass and NDVI increased by 56.1%; (2) RF and NN models outperformed SVM, with the optimal accuracies being R² = 0.709, RMSE = 0.114 g for RF, R² = 0.66, RMSE = 0.08 g for NN, and R² = 0.557, RMSE = 0.117 g for SVM. Notably, the RF model achieved the highest prediction accuracy for leaf biomass during the flowering stage (R² = 0.709, RMSE = 0.114); (3) among different water treatments, the R² values of water and drought treatments were higher 0.723 and 0.742, respectively, indicating strong adaptability. This study provides an economically effective method for monitoring winter wheat growth in the field, contributing to improved agricultural productivity and fertilization management. Full article

Wheat is one of the three major staple crops globally. The wheat spike serves as the primary structure bearing wheat grains. Spike architectures of wheat have a direct impact on the number of grains per spike, and thus the grain yield per spike. The development of wheat spike morphology is conserved to some extent in cereal crops, yet also exhibits differences, being strictly regulated by photoperiod and temperature. This paper compiles QTLs and genes related to wheat spike traits that have been published over the past two decades, summarizes the photoperiod and vernalization pathways influencing the transition from vegetative to reproductive growth, and organizes the key regulatory networks controlling spikelet and floret development. Additionally, it anticipates advancements in wheat gene cloning methods, challenges in optimizing wheat spike architecture for high yield and future directions in wheat spike trait research. Full article

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article

Choosing appropriate tillage methods and nitrogen application are important steps in the management of wheat production for obtaining high-yield and high-quality products, as well as managing the level of weed infestation. The aim of this research was to examine the impacts of three different tillage practices (conventional tillage—CT, mulch tillage—MT, and no tillage—NT), and two top dressing fertilization nitrogen levels (rational—60 kg ha⁻¹ and high—120 kg ha⁻¹) on the grain yield and quality of winter wheat, as well as on weed infestation. The present study was carried out in field experiments on chernozem luvic type soil at the Faculty of Agriculture Belgrade-Zemun Experimental field trial “Radmilovac”, in the growing seasons of 2020/2021–2022/2023. The C/N ratio in the soil was also assessed on all plots. The results showed that the number of weeds and their fresh and air-dry weights were higher on the MT and NT plots, compared to the CT plots. Therefore, the CT system has better effects on the yield (5.91 and 5.36 t ha⁻¹) and the protein content (13.3 and 13.1%). Furthermore, the grain weight per spike and the 1000-grain weight were higher in the wheat from the CT system (41.83 and 42.75 g) than from the MT (40.34 and 41.49 g) and NT (40.26 and 41.08 g) systems. Also, the crops from the CT system had higher values of grain density and grain uniformity compared to the crop from the MT and NT systems. Fertilization with a high nitrogen level (120 kg ha⁻¹) causes higher grain yield and more weediness compared with the rational level (60 kg ha⁻¹). Top dressing fertilization in each tillage system resulted in an increase in the number of weeds, but, at the same time, it also resulted in stronger competitive ability of the wheat crop against weeds. The most favorable C/N ratio occurred on the NT plots, and the least beneficial one on the CT ones. A correlation analysis showed strong negative correlations of number (r = −0.82) and fresh weed mass (r = −0.72) with yield. It is concluded that the conventional tillage practice with a low nitrogen dose manifests its superior performance in minimizing weed infestation and maximizing crop productivity. Full article

The wheat planted at the end of the rainy season in the Cerrado suffers from a strong water deficit. A selection of genetic material with drought tolerance is necessary. In improvement programs that evaluate a large number of materials, efficient, automated, and non-destructive phenotyping is essential, which requires the use of sensors. The experiment was conducted in 2016 using a phenotyping platform, where irrigation gradients ranging from 184 (WR4) to 601 mm (WR1) were created, allowing for the comparison of four genotypes. In addition to productivity, we evaluated plant height, hectoliter weight, the number of spikes per square meter, ear length, photosynthesis, and the indices calculated by the sensors. For most morphophysiological parameters, extreme stress makes it difficult to discriminate materials. WR1 (601 mm) and WR2 (501 mm) showed similar trends in almost all variables. The data validated the phenotyping platform, which creates an irrigation gradient, considering that the results obtained, in general, were proportional to the water levels. The similar trend between sensors (NDVI, PRI, and LIFT) and morphophysiological, plant growth, and crop yield evaluations validated the use of sensors as a tool in selecting drought-tolerant wheat genotypes using a non-invasive methodology. Considering that only four genotypes were used, none showed absolute and unequivocal tolerance to drought; however, each genotype exhibited some desirable characteristics related to drought tolerance mechanisms. Full article

Pre-harvest sprouting of wheat is the premature germination of ripened wheat (Triticum aestivum L.) kernels in the spike before harvest and is influenced by a combination of environmental and genetic factors, and their interaction. This greatly affects grain yield and quality, thus posing a threat to food security and sustainable agriculture. Pre-harvest sprouting has been studied for over 30 years in South Africa and remains a trait of interest in our wheat breeding programs amid climatic change. This paper therefore provides a comprehensive review of the progress made, as well as the challenges and limitations encountered, in breeding wheat for pre-harvest sprouting tolerance in South Africa. Future prospects and research directions are also discussed. Conventional breeding has been the main breeding strategy used in the country, with the success of breeding commercial wheat cultivars with durable pre-harvest sprouting tolerance for deployment in the three main wheat production regions of South Africa. Therefore, augmenting conventional breeding with molecular markers and modern genomic breeding technologies is anticipated to speed up breeding locally adapted, climate-resilient wheat varieties that balance tolerance to pre-harvest sprouting with high yield potential. This is key to realizing sustainable development goals of food security and sustainable agriculture. Full article

Plant growth-promoting rhizobacteria (PGPR) are beneficial soil microorganisms that enhance plant growth and stress tolerance through various mechanisms, including phytohormone production, EPS production, phosphate solubilization, and extracellular enzyme production. These bacteria establish endosymbiotic relationships with plants, improving nutrient availability and overall crop productivity. Despite extensive research on PGPR isolation, their practical application in agricultural fields has faced challenges due to environmental stresses and limited survival during storage. To address these limitations, the present study aimed to isolate salt-tolerant bacterial strains and formulate them with organic carriers to enhance their stability and effectiveness under saline conditions. The isolated bacterial strains exhibited high salt tolerance, surviving NaCl concentrations of up to 850 millimolar. These strains demonstrated basic key plant growth-promoting traits, including phosphate solubilization, auxin production, and nitrogen fixation. The application of carrier-based formulations with both strains, Bacillus wiedmannii (RR2) and Bacillus paramobilis (RR3), improved physiological and biochemical parameters in wheat plants subjected to salinity stress. The treated plants, when subjected to salinity stress, showed notable increases in chlorophyll a (73.3% by Peat + RR3), chlorophyll b (41.1% by Compost + RR3), carotenoids (51.1% by Peat + RR3), relative water content (77.7% by Compost + RR2), proline (75.8% by compost + RR3), and total sugar content (12.4% by peat + RR2), as compared to the stressed control. Plant yield parameters such as stem length (35.1% by Peat + RR3), spike length (22.5% by Peat + RR2), number of spikes (67.6% by Peat + RR3), and grain weight (39.8% by Peat + RR3) were also enhanced and compared to the stressed control. These results demonstrate the potential of the selected salt-tolerant PGPR strains (ST-strains) to mitigate salinity stress and improve wheat yield under natural field conditions. The study highlights the significance of carrier-based PGPR applications as an effective and sustainable approach for enhancing crop productivity in saline-affected soils. Full article

Developing early-heading wheat cultivars is an important breeding strategy to utilize light and heat resources, facilitate multiple-cropping systems, and enhance annual grain yield. Psathyrostachys huashanica Keng (2n = 2x = 14, NsNs) possesses numerous agronomically beneficial traits for wheat improvement, such as early maturity and resistance to biotic and abiotic stresses. In this study, we found that a cytogenetically stable wheat–P. huashanica 7Ns disomic addition line showed (9–11 days) earlier heading and (8–10 days) earlier maturation than its wheat parents. Morphological observations of spike differentiation revealed that the 7Ns disomic addition line developed distinctly faster than its wheat parents from the double ridge stage. To explore the potential molecular mechanisms underlying the early heading, we performed transcriptome analysis at four different developmental stages of the 7Ns disomic addition line and its wheat parents. A total of 10,043 differentially expressed genes (DEGs) were identified during spike development. Gene Ontology (GO) enrichment analysis showed that these DEGs were linked to the carbohydrate metabolic process, photosynthesis, response to abscisic acid, and the ethylene-activated signaling pathway. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that these DEGs were involved in plant hormone signal transduction (ARF, AUX/IAA, SAUR, DELLA, BRI1, and ETR), starch and sucrose metabolism (SUS1 and TPP), photosynthetic antenna proteins (Lhc), and circadian rhythm (PRR37, FT, Hd3a, COL, and CDF) pathways. In addition, several DEGs annotated as transcription factors (TFs), such as bHLH, bZIP, MADS-box, MYB, NAC, SBP, WRKY, and NF-Y, may be related to flowering time. Our findings reveal spike development-specific gene expression and critical regulatory pathways associated with early heading in the wheat–P. huashanica 7Ns addition line, and provide a new genetic resource for further dissection of the molecular mechanisms underlying the heading date in wheat. Full article

Wheat stripe rust (Puccinia striiformis f. sp. tritici, Pst) resistance and agronomic traits are crucial determinants of wheat yield. Elucidating the quantitative trait loci (QTLs) associated with these essential traits can furnish valuable genetic resources for improving both the yield potential and disease resistance in wheat. Lantian 31 is an excellent Chinese winter wheat cultivar; multi-environment phenotyping across three ecological regions (2022–2024) confirmed stable adult-plant resistance (IT 1–2; DS < 30%) against predominant Chinese Pst races (CYR31–CYR34), alongside superior thousand-kernel weight (TKW) and kernel morphology. Here, we dissected the genetic architecture of these traits using a total of 234 recombinant inbred lines (RILs) derived from a cross between Lantian 31 and the susceptible cultivar Avocet S (AvS). Genotyping with a 15K SNP array, complemented by 660K SNP-derived KASP and SSR markers, identified four stable QTLs for stripe rust resistance (QYrlt.swust-1B, -1D, -2D, -6B) and eight QTLs governing plant height (PH), spike length (SL), and kernel traits. Notably, QYrlt.swust-1B (1BL; 29.9% phenotypic variance) likely represents the pleiotropic Yr29/Lr46 locus, while QYrlt.swust-1D (1DL; 22.9% variance) is the first reported APR locus on chromosome 1DL. A pleiotropic cluster on 1B (670.4–689.9 Mb) concurrently enhanced the TKW and the kernel width and area, demonstrating Lantian 31’s dual utility as a resistance and yield donor. The integrated genotyping pipeline—combining 15K SNP discovery, 660K SNP fine-mapping, and KASP validation—precisely delimited QYrlt.swust-1B to a 1.5 Mb interval, offering a cost-effective model for QTL resolution in common wheat. This work provides breeder-friendly markers and a genetic roadmap for pyramiding durable resistance and yield traits in wheat breeding programs. Full article

T-2 toxin, a type A trichothecene mycotoxin produced by Fusarium species, is widely present in cereals and their processed products, posing a significant contaminant in food safety. To address the food safety challenges caused by this toxin, we established a dual signal enhancement by magnetic separation and split aptamer for ultrasensitive T-2 toxin detection. In this method, the introduction of magnetic graphene oxide (MGO) enhanced signal and increased sensitivity by reducing background interference. The shortened split aptamer reduces non-specific binding to MGO via decreased steric hindrance, thereby facilitating rapid target-induced dissociation and signal generation. A FAM fluorophore-labeled split aptamer probe FAM-SpA1-1 was quenched by MGO. While the fluorescence intensity remained nearly unchanged when the unlabeled split aptamer probe SpA1-2 was introduced alone, a significant fluorescence recovery was observed upon simultaneous addition of SpA1-2 and T-2 toxin. This recovery resulted from the cooperative binding of SpA1-1 and SpA1-2 to T-2 toxin, which distanced the FAM-SpA1-1 probe from MGO. Therefore, the proposed biosensor demonstrated excellent stability, reproducibility, and specificity, with a linear response range of 10–500 pM and a limit of detection (LOD) of 0.83 pM. Satisfactory recovery rates were achieved in spiked wheat (86.0–114.2%) and beer (112.0–129.6%) samples, highlighting the biosensor’s potential for practical applications in real-sample detection. This study establishes the T-2 toxin split aptamer and demonstrates a novel dual-signal enhancement paradigm that pushes the sensitivity frontier of aptamer-based mycotoxin sensors. Full article

Fusarium head blight (FHB) is a major devastating wheat fungal disease. Mycotoxins act as virulent factor for FHB progression, including deoxynivalenol (DON), 15-acetyl deoxynivalenol (15-ADON), 3-acetyl deoxynivalenol (3-ADON), deoxynivalenol-3-glucoside (D3G), and zearalenone (ZEN). To identify resistant germplasm against FHB and mycotoxin accumulation, we evaluated 99 wheat cultivars for FHB severity using point inoculation by three FHB isolates under greenhouse and field conditions. FHB severity of selected varieties evaluated in the fields were correlated with that in greenhouse (p < 0.01). Inoculated spikes from 20 varieties were examined for mycotoxin accumulation, employing an LC-MS/MS method that differentiated five mycotoxins. Five cultivars exhibited resistance to both FHB and mycotoxin accumulation, with FHB severity averaging from 13.36% to 33.37%, and DON accumulation below 2400.0 µg/kg, across various conditions. Seven dominant varieties exhibited moderate resistance to FHB and mycotoxin accumulation. FHB severity was significantly positively correlated with DON accumulation, but negatively correlated to the D3G to DON ratio, across distinct groups of FHB resistance (p < 0.01) after inoculation of three distinct isolates, although no correlation was observed within-group. In the present study, Shannong20, Huaimai20, and Sunlin were identified with resistance to both FHB and mycotoxins with superior agronomic performance, providing promising materials for improving disease resistance in breeding programs. Full article