Search Results (66)

This study evaluated the effects of winter green manure incorporation on grain yield, nitrogen uptake, and use efficiency in ratoon rice production. A two-year field experiment (2019–2021) was conducted using a split-plot design, with main plots comprising three cropping systems: fallow–ratoon rice (FA), rapeseed–ratoon rice (RA), and milk vetch–ratoon rice (MV). In the RA and MV systems, green manures were incorporated in situ, while subplots featured two ratoon rice varieties (Yliangyou 911, YLY911; Liangyou 6326, LY6326). Compared to FA treatment, RA and MV treatments significantly increased main crop yields by 16.37% and 9.31%, respectively, with corresponding annual total yield improvements of 11.34% and 7.78%. Under RA treatment, LY6326 achieved significantly higher yields than YLY911. Biomass accumulation analysis revealed that RA and MV treatments enhanced plant dry matter by 24.40% and 5.63% at heading stage, and 9.83% and 7.47% at maturity, respectively, relative to FA treatment. Green manure incorporation improved plant nitrogen content at maturity (9.42% and 10.29% for RA and MV, respectively) and panicle nitrogen accumulation (11.73% and 38.26%, respectively) compared to fallow treatment. Nitrogen use efficiency metrics demonstrated that RA and MV treatments enhanced nitrogen harvest index by 1.54% and 5.65%, respectively, while nitrogen partial factor productivity increased by 11.34% and 7.78%. Varietal comparison confirmed that LY6326 exhibited superior nitrogen accumulation and utilization compared to YLY911. These findings demonstrate that winter green manure incorporation significantly enhances grain yield and nitrogen use efficiency in ratoon rice systems, providing a scientific foundation for developing sustainable and productive rice cropping practices. Full article

In this study, we aimed to address the lack of systematic research on key collision dynamics parameters (elastic restitution coefficient) in the full mechanization of rapeseed operations, which hinders the development of precision agriculture. In this present work, the restitution coefficient of rapeseed was systematically investigated, and a predictive model (R² = 0.959) was also established by using Box–Behnken design response surface methodology (BBD-RSM). The results show that the collision restitution coefficient varies in the range of 0.539–0.649, with the key influencing factors ranked as follows: moisture content (M_c) > material layer thickness (L) > drop height (H). The EDEM simulation methodology was adopted to validate the experimental results, and the results show that there is a minimal relative error (−1% < δ < 1%) between the measured and simulated rebound heights, indicating that the established model shows a reliable prediction performance. Moreover, by comprehensively analyzing stress, strain, and energy during the collision process between rapeseed and Q235 steel, it can be concluded that the process can be divided into five stages—free fall, collision compression, collision recovery, rebound oscillation, and rebound stabilization. The maximum stress (1.19 × 10⁻² MPa) and strain (6.43 × 10⁻⁶ mm) were observed at the beginning of the collision recovery stage, which can provide some theoretical and practical basis for optimizing and designing rapeseed machines, thus achieving the goals of precise control, harvest loss reduction, and increased yields. Full article

The gleyed paddy soils in subtropical China, characterized by poor structure, high reductive substances, and low fertility, pose challenges to sustainable agriculture. This study investigates the improvement effects of applying rapeseed green manure in combination with biochar or vermicompost through field experiments, aiming to provide a theoretical basis for the organic improvement of gleyed paddy soils. The experiment included four treatments: control (CK), rapeseed green manure (GM), GM + biochar (GMB), and GM + vermicompost (GMVC). Soil physicochemical properties, aggregate stability, and fungal communities were analyzed after rice harvest. GM significantly increased the total nitrogen (TN) content in the 0–10 cm soil layer and decreased the Fe²⁺ and total glomalin-related soil protein (T-GRSP) contents. GMVC further increased the pH value, available potassium (AK) content, and Shannon index in the 0–10 cm soil layer, decreased the available phosphorus (AP) content, and increased the proportion of macro-aggregates (>2000 µm) and decreased the fractal dimension (D) in the 10–20 cm soil layer. Compared with GMVC, GMB more significantly increased the soil organic carbon content and regulated the ratio of EE-GRSP/T-GRSP in the 0–10 cm soil layer. Fungal community analysis showed Ascomycota dominance. Pearson analysis showed Westerdykella enrichment significantly correlated with reduced T-GRSP. Monte Carlo tests identified pH and SOC as key factors shaping fungal communities. The GMB strategy mitigates reductive stress, enhances nutrient availability, and activates microbial functionality. These findings offer insights and frameworks for sustainable soil management in subtropical rice agroecosystems. Full article

Telemetry systems are increasingly finding applications in agriculture for a variety of tasks. These systems assist farmers in optimizing farm processes. By leveraging these technologies, energy resources can be used more efficiently, leading to reduced environmental pollution. The primary objective of this research is to analyze telemetry data and explore ways to enhance the efficiency of combine harvesters (CHs). For this study, data from the Lexion 750 TT CH equipped with a crawler chassis was selected. Harvesting operations were conducted across fields growing popular plant types in Lithuania, including wheat, barley, rapeseed, oats, corn, and beans. The selected CH was also equipped with a remote monitoring system for tracking machine parameters. During the research, the structure of the time distribution of the work and the consumed fuel was analyzed. The highest operational efficiency—defined as the proportion of time spent on productive harvesting tasks—was 78%, observed during the oat harvest, when the unloading while harvesting, unloading while idle, harvesting, and headland turns were 3%, 2%, 64%, and 9%, respectively. The lowest efficiency, 56%, occurred during wheat harvesting. It was found that harvesting 899.32 ha of six different plant species with the tested CH produces 46.11 t of GHG emissions in CO_2eq. The largest part of the emission in CO_2eq was released during direct harvesting, with the engine operating at 1800–1900 min⁻¹. However, as much as 30% of the time and 11.2% of fuel was consumed by the CH for non-harvesting activities. In conclusion, attention should be paid to reducing the inefficient use of CH time. In this way, technological operations would not only be carried out more rationally, but also environmental pollution would be reduced, and in the case of this study, we could potentially reduce CO_2eq emissions by more than 10%. Full article

Accurate estimation of rapeseed yield is crucial for harvesting decisions and improving efficiency and output. Machine learning (ML) models driven by remote sensing data are widely used for yield prediction. This study explores the generality of feature-based rapeseed yield prediction models across different varieties and years. Seven vegetation indices (VIs) and twenty-four texture features (TFs) were calculated from UAV-based imagery. Pearson’s correlation coefficient was used to assess variable sensitivity at different growth stages, and the variable importance score (VIP) from the random forest (RF) model was used for feature selection. Three ML regression methods—RF, support vector regression (SVR), and partial least squares regression (PLSR)—were applied using the single-stage VI, selected multi-stage VI, and multivariate VI-TFs for yield prediction. The best yield model was selected through cross-validation and tested for temporal fit using cross-year data. Results showed that the multi-stage VI and RF model achieved the highest accuracy in the training dataset (R² = 0.93, rRMSE = 7.36%), while the multi-stage VI and PLSR performed best in the test dataset (R² = 0.62, rRMSE = 15.20%). However, this study demonstrated that the addition of TFs could not enhance the robustness of rapeseed yield estimation. Additionally, the model updating strategy improved the RF model’s temporal fit, increasing R² by 25% and reducing the rRMSE to below 10%. This study highlights the potential of the multi-stage VI for rapeseed yield prediction and offers a method to improve the generality of yield prediction models over multiple years, providing a practical approach for meter-scale yield mapping and multi-year prediction. Full article

Rapeseed (Brassica napus L., 2n = 38) is an important oil crop worldwide, providing vegetable oil and biofuel. Despite improvements in breeding, rapeseed’s harvest index and yield remain lower than other major crops. Plant height (PH) and first-branch height (FBH) are crucial plant architecture traits affecting yield, lodging resistance and efficiency of mechanical harvesting. Phenotypic analysis of 125 rapeseed accessions across four environments revealed wide variation in PH (100–198 cm) and FBH (15.56–112.4 cm), with high broad-sense heritability (H² = 81.59% for PH, 77.69% for FBH), and significant positive correlations between traits. To understand the genetic control of PH and FBH, a genome-wide association study (GWAS) of a natural population was conducted, covering 2,131,705 genome variants across four environments. The 13 QTLs for PH and 15 for FBH were identified. Meta-analysis revealed that 28.57% of these loci overlapped with previously reported QTLs. Haplotype analysis confirmed significant effects of these loci on the traits. Candidate genes for PH and FBH, respectively, were identified based on linkage disequilibrium and functional predictions. However, five novel loci lacked nearby annotated genes. The candidate genes are linked to traits in Arabidopsis and other species, as well as to phytohormone response and cell development, and cell development. Notably, MOS1 gene copies (BnaA03G0481200ZS and BnaC07G0459400ZS) were associated with PH and FBH, indicating their multifunctional potential. Additionally, BnaA05G0163200ZS, with no functional annotation, emerged as a crucial gene for plant architecture. This study provides new genetic insights and may enhance marker-based breeding for ideotypes in rapeseed. Full article

This study performed in-season yield prediction, about 2–3 months before the harvest, for cereals and rapeseed at the province level in Poland for 2009–2024. Various models were employed, including machine learning algorithms and multiple linear regression. The satellite-derived normalized difference vegetation index (NDVI) and climatic water balance (CWB), calculated using meteorological data, were treated as predictors of crop yield. The accuracy of the models was compared to identify the optimal approach. The strongest correlation coefficients with crop yield were observed for the NDVI at the beginning of March, ranging from 0.454 for rapeseed to 0.503 for rye. Depending on the crop, the highest R² values were observed for different prediction models, ranging from 0.654 for rapeseed based on the random forest model to 0.777 for basic cereals based on linear regression. The random forest model was best for rapeseed yield, while for cereal, the best prediction was observed for multiple linear regression or neural network models. For the studied crops, all models had mean absolute errors and root mean squared errors not exceeding 6 dt/ha, which is relatively small because it is under 20% of the mean yield. For the best models, in most cases, relative errors were not higher than 10% of the mean yield. The results proved that linear regression and machine learning models are characterized by similar predictions, likely due to the relatively small sample size (256 observations). Full article

Bee venom is a complex natural beekeeping product, traditionally used in apitherapy, with a wide spectrum of pharmacological properties. Research on the mineral content of bee venom is limited and challenging to compare across studies due to the varying regions where they are conducted. Our study aimed to assess the mineral content of bee venom and how supplementary feeding of bee colonies with probiotic products, essential oils, as well as rapeseed and acacia nectar and pollen, affects the mineral content and antioxidant activity of the venom. The parameters analyzed included moisture, pH, dry matter, ash, impurities, and levels of macro and micro elements and antioxidant activity. The moisture content of the samples was 10–22%, and pH ranged between 5.84 to 6.41. The macro element content of the venom showed that potassium was the most abundant macro element, followed by calcium, magnesium, and phosphorus. Pb was identified in samples collected indicating lead pollution in the area where the hives were located in the case of the three harvests. In all samples, the highest DPPH radical scavenging activity was observed at a concentration of 2.00 mg/mL, with sample V6 showing the maximum value of 87.05%. Full article

The decline of pollinators in agricultural environments poses a significant threat to pollination ecosystem services. Wildflower strips are proposed as a strategy to support pollinator populations and enhance their species richness and diversity. We investigated the efficacy of flowering plant mixture in maintaining aculeate pollinator diversity (wild bees, predatory wasps, and their kleptoparasites) within intensively managed agricultural environments where rapeseed is a common rotational crop. Over four years, pollinators were counted five times per season using 250 m transect walks. Our results demonstrated that the diversity and the evenness of species abundance distribution of aculeate pollinators were higher in the sown wildflower strips, whereas mean abundance per transect was greater in the remnants of semi-natural grassland. The low diversity and evenness within the aculeate pollinator assemblage of the semi-natural habitat were attributed to the dominance of the sweat bee Lasioglossum pauxillum, which thrived on mass-flowering rapeseed and concentrated in the flowering grassland fragments after the rapeseed harvest. We conclude that wildflower strips enriched with sown flowering plant mixtures effectively enhance pollinator diversity. Furthermore, both wildflower strips and preserved patches of unmanaged or minimally managed semi-natural grassland habitats can essentially contribute to maintaining pollination ecosystem services within intensive agricultural environments. Full article

Ascorbic acid (AsA) is an important antioxidant for human health. The concept of “oil-vegetable-duel-purpose” can significantly enhance the economic benefits of the rapeseed industry. Rapeseed, when utilized as a vegetable, serves as a valuable food source of AsA. In this study, we integrated transcriptome and metabolome analyses, along with substrate feeding, to identify the L-galactose pathway as the primary source for AsA production, which is primarily regulated by light. Through seven different photoperiod treatments from 12 h/12 h (light/dark) to 24 h/0 h, we found that AsA content increased with longer photoperiods, as well as chlorophyll, carotenoids, and soluble sugars. However, an excessively long photoperiod led to photooxidative stress, which negatively affected biomass accumulation in rapeseed seedlings and subsequently impacted the total accumulation of AsA. Furthermore, different enzymes respond differently to different photoperiods. Analysis of the correlation between the expression levels of AsA biosynthesis-related genes and AsA content highlighted a dynamic balancing mechanism of AsA metabolism in response to different photoperiods. The study revealed that the 16 h/8 h photoperiod is optimal for long-term AsA accumulation in rapeseed seedlings. However, extending the photoperiod before harvest can enhance AsA content without compromising yield. These findings offer novel insights into an effective strategy for the biofortification of AsA in rapeseed. Full article

As a result of the energy crisis due, among other things, to climate change, most developed countries have taken steps with the main aim—among other things—of increasing the use of green energy sources that do not rely on fuels (including primarily liquid fuels) but use renewable energies. Plant biomass is a versatile substrate that can be used in many areas of the economy and production, but also for the production of various types of fuel. These range from rapeseed oil used as a component of biodiesel or maize starch for ethanol production to typically cellulosic plants such as energy willow, which can be used for direct combustion. The floodplain is home to this type of vegetation. It is characterized by great diversity in terms of geometric dimensions and mechanical and morphological properties. In addition, the location (easy access to water and sunlight) influences its potential energy value. Vegetation, thanks to favorable conditions, can achieve large weight gains in a relatively short period of time. Therefore, its properties should be carefully recognized in order to make more efficient use of energy and operating equipment used during harvesting. This paper presents an analysis of the changes in the elasticity of willow branches over a period of 16 days following harvesting. The changes were analyzed for branches taken from three different shrubs at three different plant height levels during the post-growth period. Based on the measurements carried out, the elastic modulus E of the shoots was estimated. The average modulus of elasticity ranged from about 4500 two days after cutting to about 5500 MPa 16 days after cutting and showed high variability, reaching even CV = 37%, both within a given shrub and depending on the measurement date. The results presented here indicate a high natural variability of mechanical parameters even within the same plant. Full article

Honey contains natural biologically active compounds, and its preventive and healing properties are primarily linked to its antioxidant activity. The antioxidant properties of honey can be related to the botanical origin and content of phenolic compounds. We tested 84 honey samples from Poland, representing eight honey varieties: acacia, phacelia, buckwheat, linden, rapeseed, heather, goldenrod, and honeydew. High-performance liquid chromatography with photodiode-array detection (HPLC-DAD) was used to determine the phenolic compound composition of honey extracts. Total phenolic compounds (TPC) and DPPH radical-scavenging activity were also evaluated. We detected vanillin aldehyde, vanillic acid, caffeic acid, p-coumaric acid, and trans-ferulic acid, as well as flavonoid pinocembrin, in all honey varieties. The results of our study showed that honeys with high antioxidant activity were characterized by significantly higher total phenolic compounds content. Neither clustering method nor principal component analysis (PCA) showed clear separation of each honey variety, possibly due to high intra-variety diversities. We suppose that the variability of qualitative and quantitative phenolic compound composition within honey varieties may result from the region of origin, secondary nectar sources, and the time of harvest. Full article

Aiming to address the problem of low recognition accuracy in the current cleaning loss detection system of rapeseed harvesters, a rapeseed cleaning loss detection system was developed using the energy distinction method based on the principle of impact piezoelectricity. A signal processing circuit, centered around a hardware integral circuit and a triple voltage comparison circuit, was designed. The energy of the impact signals generated by rapeseed kernels and impurities was calculated through hardware integration. The distinction threshold for the energy of the impact signals generated by kernels and impurities under the operating wind speed of the cleaning system was found through experiments, and a fitting model relating the fan speed to the distinction threshold was constructed. A loss detection and counting system for rapeseed kernels was designed to realize the statistics and real-time display of rapeseed kernels regarding cleaning loss. Performance verification tests were conducted on mixtures of rapeseed kernels and impurities with different mixing ratios, and a field test was carried out on the platform of a 4LZY-5.0Z rapeseed combine harvester. The test results showed that the accuracy of the designed loss detection system for kernel identification was more than 91.6%. Under operating conditions of 700, 900 and 1200 r/min fan speeds in the combine harvester cleaning system, the relative errors of the loss detection system compared to manual detection were 5%, 4.7% and 3.8%, respectively. The developed loss detection system for rapeseed kernels has high detection accuracy and good overall performance, which means it can provide feedback information for the control of the harvester. Full article

The impurity rate is one of the core indicators for evaluating the quality of rapeseed combine harvesters. It directly affects the economic value of rapeseed. At present, the impurity rate of rapeseed combine harvesters mainly relies on manual detection during shutdown, which cannot be monitored in real time. Due to the lack of accurate real-time impurity rate data, the operation parameters of rapeseed harvesters mainly depend on the driver’s experience, which results in large fluctuations in field harvest quality. In this research, a machine vision-based method of impurity detection for rapeseed harvesters, including an image acquisition device and impurity detection algorithm, was developed. The image acquisition device is equipped with a direct-current light source, a conveyor belt, and an industrial camera for taking real-time images of rapeseed samples. Based on the color and shape characteristics of impurity and rapeseed, the detection of rapeseed and impurity was achieved. A quantitative model for the rapeseed impurity rate was constructed to calculate the real-time impurity rate of machine-harvested rapeseed accurately. The field experiment showed that the average accuracy of the detection system for the impurity rate in rapeseed was 86.36% compared with the manual detection data. The impurity detection system proposed in this paper can swiftly and effectively identify rapeseed and impurity and accurately calculate the impurity rate, which can be applied to rapeseed harvesters to provide data support for the adjustment of operating parameters. Full article

The objective of this study was to evaluate the antimicrobial effect of direct cold atmospheric plasma (CAPP) treatment for pre-harvest application using four different crop species: Hordeum vulgare L. (barley), Triticum aestivum L. (wheat), Brassica napus L. (rapeseed) and Lupinus angustifolius L. (lupine). The model bacterium Bacillus atrophaeus served as a proxy for spore-forming plant pathogens on the seed surface. After semi-dry inoculation of spores onto the seeds, treatment with two different plasma sources, a volume-dielectric barrier discharge and a corona discharge, and different exposure times was carried out. Subsequently, recovery of viable spores from the seeds’ surfaces was performed. Moreover, seed viability was determined based on maximum germination, as well as water contact angle as a measure for seed surface hydrophilicity. Direct CAPP treatment was efficient in reducing viable spores of B. atrophaeus with no significant differences between the plasma sources, reaching a mean inactivation of 1 log₁₀ CFU/mL across all treatment times and crops species. Maximum germination of seeds was not negatively affected under any treatment condition. Seed hydrophilicity was increased for both plasma sources tested. Overall, this study provides valuable information on the efficiency of direct CAPP treatment of seeds with the purpose of seed hygienization with the premise of unaltered seed vitality and evaluates the potential application in comparison with previous investigated CAPP methods. Full article