Search Results (63)

Identifying superior genotypes in multi-environment trials is crucial for accelerating cultivar improvement and breeding innovation. This study evaluated the yield potential of 29 maize hybrids (including the control) across 10 trial locations in mid-to-high altitude regions of Yunnan Province from two growing seasons (2023–2024), aiming to recommend high-yielding, stable, and widely adapted maize varieties. Analysis of variance indicated that genotype, environment, and their interaction all had highly significant effects (p < 0.001) on maize yield, with environmental factors accounting for the primary source of variation; in 2023 and 2024, 63.79% and 64.15% of the total variation were explained, respectively. The grain yield of the maize hybrids ranged from 8873 kg/ha to 12,089 kg/ha, with the highest yield over the two consecutive years being 11,783 kg/ha (XR-399). Yield mean analysis identified the top-performing hybrids annually: in 2023, these were G28, G13, G22; in 2024, they included G5, G13, G4. In the GGE biplot analysis, E2 (Binchuan), E5 (Lijiang), E7 (Shilin), and E8 (Xuanwei) were the most distinguishable and representative test environments. The “mean vs. stability” GGE biplot indicated that G22 (LS-2305), G9 (LS-2303), and G13 (XR-399) exhibited consistent high yields and stability across years. Based on the “Which-Won-Where” GGE biplot, G27 (SS-2205) and G13 (XR-399) were identified as the optimal hybrids for each mega-environment, with G13 (XR-399) emerging as the most outstanding. Therefore, these findings confirm that the GGE biplot method is effective for screening high-yielding, stable hybrids and identifying representative test environments, thereby providing a scientific foundation for maize breeding work in the region. Full article

This study systematically investigates the thickness-dependent mechanical properties of electroplated copper foils with fine-grained (FG-Cu) and columnar nanotwinned (NT-Cu) microstructures. Tensile testing across a thickness range of 5–30 μm revealed that NT-Cu exhibits superior mechanical stability, with significantly lower reductions in both ultimate tensile strength (UTS) and yield strength (YS) compared to FG-Cu. The UTS of the 30 μm thick FG-Cu foil was measured at 651 MPa, increasing to 792 MPa at a thickness of 5 μm. In contrast, the UTS of NT-Cu foils only rose from 624 MPa at 30 μm to 663 MPa at 5 μm. A similar trend was observed for the YS. Microstructural analysis confirmed that NT-Cu maintains a stable columnar grain structure with minimal grain growth, contributing to its resistance to thickness-induced strength loss. These findings highlight NT-Cu as a promising candidate for applications requiring consistent mechanical performance across varying foil thicknesses. Full article

Hyperspectral images (HSIs) provide rich spectral–spatial information and support applications in remote sensing, agriculture, and medicine, yet their development is hindered by data scarcity and costly acquisition. Diffusion models have enabled synthetic HSI generation, but conventional integer-order solvers such as Denoising Diffusion Implicit Models (DDIM) and Pseudo Linear Multi-Step method (PLMS) require many steps and rely mainly on local information, causing error accumulation, spectral distortion, and inefficiency. To address these challenges, we propose Integer–Fractional Alternating Diffusion Sampling (IFADiff), a training-free inference-stage enhancement method based on an integer–fractional alternating time-stepping strategy. IFADiff combines integer-order prediction, which provides stable progression, with fractional-order correction that incorporates historical states through decaying weights to capture long-range dependencies and enhance spatial detail. This design suppresses noise accumulation, reduces spectral drift, and preserves texture fidelity. Experiments on hyperspectral synthesis datasets show that IFADiff consistently improves both reference-based and no-reference metrics across solvers without retraining. Ablation studies further demonstrate that the fractional order $\alpha$ acts as a controllable parameter: larger values enhance fine-grained textures, whereas smaller values yield smoother results. Overall, IFADiff provides an efficient, generalizable, and controllable framework for high-quality HSI generation, with strong potential for large-scale and real-time applications. Full article

We propose a local–global attention fusion network for benign–malignant discrimination of ovarian tumors in color Doppler ultrasound (CDFI). The framework integrates three complementary modules: a local enhancement module (LEM) to capture fine-grained texture and boundary cues, a Global Attention Module (GAM) to model long-range dependencies with flow-aware priors, and a Fusion Discriminator (FD) to align and adaptively reweight heterogeneous evidence for robust decision-making. The method was evaluated on a multi-center clinical dataset comprising 820 patient cases (482 benign and 338 malignant), ensuring a realistic and moderately imbalanced distribution. Compared with classical baselines including ResNet-50, DenseNet-121, ViT, Hybrid CNN–Transformer, U-Net, and SegNet, our approach achieved an accuracy of 0.923, sensitivity of 0.911, specificity of 0.934, AUC of 0.962, and F1-score of 0.918, yielding improvements of about three percentage points in the AUC and F1-score over the strongest baseline. Ablation experiments confirmed the necessity of each module, with the performance degrading notably when the GAM or the LEM was removed, while the complete design provided the best results, highlighting the benefit of local–global synergy. Five-fold cross-validation further demonstrated stable generalization (accuracy: 0.922; AUC: 0.961). These findings indicate that the proposed system offers accurate and robust assistance for preoperative triage, surgical decision support, and follow-up management of ovarian tumors. Full article

Weeds are a significant factor affecting crop yield and quality. Herbicides have made crucial contributions to ensuring stable and high grain production, but the low effective utilization rate and short duration of traditional formulations have led to excessive application and a range of ecological and environmental issues. Nanoherbicides, particularly carrier-coated systems, can simultaneously leverage the small size, large specific surface area, and high permeability of nanoparticles, as well as the multifunctionality of carriers, to synergistically enhance the efficacy and safety of the formulations. This provides a scientific and promising strategy for overcoming the functional deficiencies of traditional formulations. Nevertheless, there are currently relatively few articles that systematically review the research progress and performance advantages of nanoherbicides. This review provides a concise overview of the preparation methods and structural characteristics of nanoherbicides. It primarily highlights the classification of carrier-coated nanoherbicides, along with representative studies and their distinctive properties across various categories. Based on this foundation, the performance advantages of nanoherbicides are systematically summarized. Finally, the major challenges and future prospects in this research field are proposed. This review offers valuable insights and methodological guidance for the design and rational application of efficient, environmentally friendly nanoherbicides. Full article

Beer is the third most popular beverage in the world, and its production is distributed uniformly between the biggest continents. Considering the environmental aspects, the utilization of brewing by-products, mainly brewers’ spent grain (BSG), is essential on a global scale. The beer revolution, lasting over a few decades, significantly diversified the beer market in terms of styles, and therefore, also its by-products, which should be characterized appropriately prior to further application. Herein, the presented study investigated the unprecedented number of 22 different variants of brewers’ spent grain, yielded from the production of various beer styles, enabling their proper comparison. A comprehensive by-product characterization revealed an almost linear relationship (Pearson correlation coefficients exceeding 0.9) between the color parameters (L*, a*, browning index) of beer and generated spent grain, enabling a prediction of BSG appearance based on beer color. Applying wheat or rye malts increased the content of extractives by over 40%, reducing cellulose content by as much as 45%. Thermal treatments of malts (kilning or smoking) also reduced extractive content and limited antioxidant activity, often by over 30%. A lack of husk for wheat or rye reduced the crystallinity index of spent grain by 21–41%, while the roasting of barley efficiently decomposed the less stable compounds and maintained the cellulose crystalline structure. All the analyzed BSG samples were characterized by low volatile emissions and very limited antimicrobial activity. Therefore, their harmfulness to human health and the environment is limited, broadening their potential application range. Full article

Soybean (Glycine max L.) is a vital grain and oil crop, serving as a primary source of edible oil, plant-based protein, and livestock feed. Its production is crucial for ensuring global food security. However, soybean yields are severely impacted by various diseases, and the development of disease-resistant cultivars remains the most sustainable strategy for mitigating these losses. While stable genetic transformation is a common approach for studying gene function, virus-induced gene silencing (VIGS) offers a rapid and powerful alternative for functional genomics, enabling efficient screening of candidate genes. Nevertheless, the application of VIGS in soybean has been relatively limited. In this study, we established a tobacco rattle virus (TRV)-based VIGS system for soybean, utilizing Agrobacterium tumefaciens-mediated infection. The TRV vector was delivered through cotyledon nodes, facilitating systemic spread and effective silencing of endogenous genes. Our results demonstrate that this TRV–VIGS system efficiently silences target genes in soybean, inducing significant phenotypic changes with a silencing efficiency ranging from 65% to 95%. Key genes, including phytoene desaturase (GmPDS), the rust resistance gene GmRpp6907, and the defense-related gene GmRPT4, were successfully silenced, confirming the system’s robustness. This work establishes a highly efficient TRV–VIGS platform for rapid gene function validation in soybean, providing a valuable tool for future genetic and disease resistance research. Full article

In this study, a combined localization experiment was performed on different nitrogen application rates in rice–wheat rotation. Rice cultivar Nanjing 5718 and wheat variety Yangmai 25 were employed in this two-season study, with six and five distinct nitrogen rates designed during the rice and wheat growing seasons, respectively. Thus, a total of 30 N rate combinations were formed across the two seasons. Our findings indicate that when current-season N inputs ranged from 0 to 240 kg ha⁻¹, residual N from the preceding season contributed significantly to yield improvement (5.58–18.96% increase) for subsequent crops, primarily through enhanced panicle formation and the number of grains per spike. Conversely, high current-season N rates (360–420 kg ha⁻¹) lead to reduced yields (4.61–5.81%) in the following cropping cycle under identical N management practices. Maximizing annual crop production was achieved with a combined N regimen of 264.63 kg ha⁻¹ (rice) and 254.89 kg ha⁻¹ (wheat), yielding 14.21 t ha⁻¹. Notably, current-season N levels exhibited significant correlations with starch and protein content in both rice and wheat, whereas previous-season N application showed no comparable relationships. Furthermore, soil N storage remained stable, and the highest N use efficiency was observed under the total annual N input of 547.7 kg ha⁻¹ (rice + wheat). Full article

Triticale (× Triticosecale Wittmack) is a valuable dual-purpose crop due to its adaptability to marginal environments and its potential for both high-quality grain and forage production. However, a comprehensive evaluation of its forage quality characteristics and agronomic performances is still needed. This study evaluated the grain and forage yield potentials and nutritional compositions of 11 triticale genotypes over two consecutive years in a semi-arid region located in Shanxi province, China. Forage quality was assessed using several key parameters, including nutrient composition, fiber digestibility, mineral content, and energy density, while grain quality parameters, including nutrient composition as well as carbohydrate and fiber characteristics, were also analyzed. Significant genetic variation was observed in these traits, indicating the influence of genotype–environment interactions on these traits. The tested genotypes exhibited grain yields ranging from 4.83 to 6.92 t ha⁻¹ and fresh forage biomass yields between 20.06 and 29.78 t ha⁻¹, demonstrating their potential for sustainable forage and grain production under semi-arid conditions. Genotypes from our breeding programs, including Shengnongsicao 1 and Jinsicao 1, demonstrated superior adaptability, maintaining stable forage and grain yield potentials under adverse conditions. Their favorable nutritional characteristics further enhance their suitability for semi-arid livestock systems. High levels of essential minerals, particularly calcium and potassium, further enhanced the nutritional value of these genotypes. These results provide valuable insights for triticale breeding programs and suggest triticale’s potential as a reliable crop in semi-arid regions, where maximizing land productivity is essential. Full article

This study presents a novel approach to managing the cost–time–quality trade-off in modern agriculture by integrating fuzzy logic with a genetic algorithm. Agriculture faces significant challenges due to climate variability, economic constraints, and the increasing demand for sustainable practices. These challenges are compounded by uncertainties and risks inherent in agricultural processes, such as fluctuating yields, unpredictable costs, and inconsistent quality. The proposed model uses a fuzzy multi-objective optimization framework to address these uncertainties, incorporating expert opinions through the alpha-cut technique. By adjusting the level of uncertainty (represented by alpha values ranging from 0 to 1), the model can shift from pessimistic to optimistic scenarios, enabling strategic decision making. The genetic algorithm improves computational efficiency, making the model scalable for large agricultural projects. A case study was conducted to optimize resource allocation for rice cultivation in Asia, barley in Europe, wheat globally, and corn in the Americas, using data from 2003 to 2025. Key datasets, including the USDA Feed Grains Database and the Global Yield Gap Atlas, provided comprehensive insights into costs, yields, and quality across regions. The results demonstrate that the model effectively balances competing objectives while accounting for risks and opportunities. Under high uncertainty (α = 0\alpha = 0α = 0), the model focuses on risk mitigation, reflecting the impact of adverse climate conditions and market volatility. On the other hand, under more stable conditions and lower market volatility conditions (α = 1\alpha = 1α = 1), the solutions prioritize efficiency and sustainability. The genetic algorithm’s rapid convergence ensures that complex problems can be solved in minutes. This research highlights the potential of combining fuzzy logic and genetic algorithms to transform modern agriculture. By addressing uncertainties and optimizing key parameters, this approach paves the way for sustainable, resilient, and productive agricultural systems, contributing to global food security. Full article

Nitrogen dioxide (NO₂) impacts climate, air quality, soil nitrogen fixation, and crop production, yet its transboundary impacts remain unclear. This study combines 15 global datasets to assess nitrogen’s transboundary impacts on crop yields and soil health. We use machine learning to develop yield prediction models for major grain crops (maize, rice, soybean, and wheat) affected by NO₂. Our findings indicate stable soil nitrogen fixation in China from 2015 to 2020, although overgrazing and deforestation may cause declines. Increasing soil total nitrogen content by 0.62–2.1 g/kg can reduce NO₂ by 10–30%. Our research indicates that the current agricultural environments for major grain crops (58.5–94.2%) have already exceeded the NO₂ concentration range that crops can tolerate, particularly in regions near northern urban clusters. This highlights the need for regional interventions, such as precision nitrogen fertilizer management, to enhance both soil nitrogen fixation and crop yields. Scenario analysis suggests that NO₂ control can boost maize and rice yields in a greener context, while increasing total nitrogen content improves wheat and soybean yields. This provides a solution for advancing sustainable agriculture by linking nitrogen cycle management with improved crop yields and environmental sustainability. Full article

To ensure food and feed security, modern maize hybrids must not only perform well under changing climate conditions but also consistently achieve higher and stable yields, exhibit maximum tolerance to stress factors, and produce high quality grains. In a study conducted in 2022 and 2023, 50 maize hybrids were developed from crosses of five elite (highly productive) inbred lines and ten lines possessing favorable genes for carotenoid content. These hybrids were tested under particularly unfavorable conditions for maize cultivation. The aim was to identify which lines effectively transmit the desired traits to the offspring (general combining ability—GCA), and to identify superior hybrids in terms of productivity, adaptability, and quality (specific combining ability—SCA). The study revealed that total carotenoids ranged from 2.30 to 40.20 μg/g for the inbred lines and from 7.45 to 25.08 μg/g for hybrids. A wider distribution of values was observed in the inbred lines compared to the hybrids for key carotenoids such as lutein, zeaxanthin, β-cryptoxanthin, and β-carotene. Among the hybrids, notable performers in yield, adaptability, and carotenoid content included E390×D302, A452×D302, and A447×D302. The paternal inbred line D302 exhibited a high general combining ability for yield (1446 kg ha⁻¹) and, when crossed with several inbred lines, produced hybrids with enhanced yields and higher levels of zeaxanthin, lutein, and β-carotene, as well as improved unbroken plants percent. Full article

Yield-related traits have higher heritability and lower genotype-by-environment interaction, making them more suitable for genetic studies in comparison with the yield per se. Different populations have been developed and employed in QTL mapping; however, the use of reciprocal SSSLs is limited. In this study, three kinds of bi-parental populations were used to investigate the stable and novel QTLs on six yield-related traits, i.e., plant height (PH), heading date (HD), thousand-grain weight (TGW), effective tiller number (ETN), number of spikelets per panicle (NSP), and seed set percentage (SS). Two parental lines, i.e., japonica Asominori and indica IR24, their recombinant inbred lines (RILs), and reciprocal single-segment substitution lines (SSSLs), i.e., AIS and IAS, were genotyped by SSR markers and phenotyped in four environments with two replications. Broad-sense heritability of the six traits ranged from 0.67 to 0.94, indicating their suitability for QTL mapping. In the RIL population, 18 stable QTLs were identified for the six traits, 4 for PH, 6 for HD, 5 for TGW, and 1 each for ETN, NSP, and SS. Eight of them were validated by the AIS and IAS populations. The results indicated that the allele from IR24 increased PH, and the alternative allele from Asominori reduced PH at qPH3-1. AIS18, AIS19, and AIS20 were identified to be the donor parents which can be used to increase PH in japonica rice; on the other hand, IAS14 and IAS15 can be used to reduce PH in indica rice. The allele from IR24 delayed HD, and the alternative allele reduced HD at qHD3-1. AIS14 and AIS15 were identified to be the donor parents which can be used to delay HD in japonica rice; IAS13 and IAS14 can be used to reduce HD in indica rice. Reciprocal SSSLs not only are the ideal genetic materials for QTL validation, but also provide the opportunity for fine mapping and gene cloning of the validated QTLs. Full article

The excessive application of nitrogen (N) fertilizer can result in soil and water pollution, thereby negatively impacting the ecological environment. However, reducing the amount of N fertilizer may lead to a decrease in crop yield. Two years’ experiment (2021 and 2022) investigates the influence of one-time reduced basal application of controlled-release N fertilizer (CRU) and increased planting density on the grain yield and N utilization characteristics of mechanically transplanted japonica rice. Nanjing 5718 was used as the experimental material. Under the condition of 225 kg ha⁻¹ of N, three controlled-release periods of CRUs (60d, 80d, 100d) and three planting densities (6, 8, and 10 seedlings/hole) were combined, totaling nine combinations. Moreover, a conventional split fertilization treatment with 300 kg ha⁻¹ of N and a planting density of 4 seedlings/hole was set as the control (CK). The yield, dry matter accumulation, N accumulation, and N utilization efficiency were evaluated. The research findings demonstrate that the CRU80-6 treatment exhibited the highest efficacy among all N reduction and density increase treatments, resulting in a significant yield increase of 3.1–10.3% compared to other treatments. After the jointing stage, the CRU80-6 treatment exhibited the highest dry matter accumulation compared to other treatments, with an increase ranging from 0.8% to 13.6%, and was significantly lower than that of the CK by 4.3% to 5.0%. The N accumulation and translocation traits of the CRU80-6 treatment closely resembled those of CK. However, both N recovery efficiency (NRE) and N agronomic efficiency (NAE) exhibited a remarkable increase compared to CK, with an average enhancement in NRE of 30.01%. Therefore, we contend that the CRU80-6 treatment, with a 25% reduction in N input, can ensure efficient N utilization and attain a relatively stable grain yield. Full article

The multi-environment evaluation of wheat genotypes for grain yield is an integral part of germplasm enhancement since it plays a pivotal role in sustainable production. A total of 178 winter wheat cultivars were evaluated across 20 environments in Sweden from 2016 to 2020, with 52 to 59 cultivars tested per year as part of the Swedish National Trials (Sverigeförsöken). The genotypes were evaluated for grain yield performance with and without fungicide treatments. Additive main-effects and multiplicative interaction (AMMI) and genotype plus genotype-by-environment interaction (GGE) biplot methods were explored to estimate the genotype-by-environment interaction (GEI) for grain yield performance. ANOVA revealed a significant variation between treatments, genotypes in all years, and GEI in all years except 2018. The majority of the explained variance came from the environment, with a range of 61–88% across the five-year trial. The 20 sites were grouped into two to four mega-environments in the yearly studies. From the fungicide-treated trials, G 0512LT3, Informer, SG SU1563-15, LG Imposanto, and Pondus were identified as the most stable and high-yielding cultivars each year. From the fungicide-untreated trials, Informer, Ancher Greece, RGT Saki, and Pondus were the best-performing cultivars and could be good candidates for organic wheat cultivation. Full article