Search Results (54)

Maize is a staple crop supporting global food security. Comprehensive evaluation of its phenotypic variation and environmental adaptability is urgently needed. In this study, three mutually independent maize germplasm panels with a total of 1197 hybrids were characterized using 39 standardized DUS traits: Panel A (783 hybrids, single location, 2023), Panel B (129 hybrids, two locations in the same year), and Panel C (285 hybrids, single location across two consecutive years). Tassel- and anthocyanin-related traits exhibited wide phenotypic variation and strong germplasm discrimination ability, while leaf margin anthocyanin (Char. 8) displayed rare genotypes with unique identification values. Hierarchical clustering divided all materials into eight distinct groups. PCA revealed weak overall phenotypic differentiation, which is likely driven by long-term directional selection in modern maize breeding. AMMI-based stability evaluation demonstrated that grain morphological traits (Char. 33, Char. 35) maintained stable performance across locations and years, whereas anthocyanin-related descriptors (Char. 8, Char. 14) showed severe environmental plasticity. Using the classic Multi-Trait Stability Index (MTSI), we screened top stable genotypes: 77, 69 and 52 for cross-location trials, and N90, N85 and N107 for multi-year trials. BLUP five-fold cross-validation revealed that grain morphological traits achieved high cross-location predictive correlation (r² > 0.80), whereas cross-year predictability was notably lower, reflecting greater influence of annual climatic fluctuations. Collectively, this study constructs a standardized analytical pipeline to mine routine multi-environment DUS records, identifies stable elite germplasm, and provides data-driven references for optimizing maize breeding and improving official DUS testing efficiency. Full article

Ukraine’s energy strategy under martial law necessitates decentralized local energy systems to counter electricity shortages and systemic infrastructure failures. The study develops and validates an optimization model for designing the architecture of decentralized “energy islands” based on circular bioenergy models for agricultural waste use. Empirical verification was conducted using data from the Vinnytsia region in Ukraine. The model accounts for a multi-level structure that separates micro/small generation (0.1–2.0 MW) from medium generation (1–20 MW) based on the logistical radius for raw material collection. The model incorporated the Value of Lost Load (VLL), enabling the monetization of avoided socio-economic losses from energy shortages. In addition, the coefficient of energy island sustainability (I_sred) was introduced to quantitatively assess the effectiveness of investments in terms of replacing external resources. The modeling revealed the nonlinear nature of the total cost function, enabling us to determine an optimal energy-autonomy range of 40% to 50% for communities. At this threshold, the total construction and logistics costs are minimized. The potential socio-economic losses from blackouts are effectively mitigated, as confirmed by the calculated sustainability coefficient (I_sred), which ranges from 0.78 to 0.94 across the studied communities. The resource potential assessment confirms that the region’s total potential is approaching 30 million tons of oil equivalent, driven by solid biofuels, agricultural residues, and energy crops (miscanthus, switchgrass). The classification of biomass supply chains shows that exceeding the transportation radius by more than 70 km at the meso level, or deviating from the optimal logistics lever by 20%, reduces the profitability of projects below the critical limit of 15%, which justifies strict localization within raw-material clusters. This enables local communities to eliminate natural gas consumption, reduce energy supply operating costs by 15%, and ensure the autonomous and stable operation of critical infrastructure facilities during prolonged disruptions to the national power grid. Full article

Aiming at severe soil secondary salinization, poor water retention and insufficient salt tolerance of conventional acrylic-based modifiers in arid and semi-arid regions of China, a poly(ethylene glycol) maleate citrate (PEGMC) crosslinking monomer was synthesized through esterification, and a dual covalent–hydrogen crosslinked P(PEGMC/AA) hydrogel was fabricated via free radical copolymerization with acrylic acid (AA). The hydrogel was characterized by NMR, FTIR, SEM, TGA and elemental mapping, while its binding mechanism with saline–alkali ions was elucidated through DFT calculations and molecular dynamics simulations. Its amelioration performance was evaluated through swelling, soil water retention, desalination and pot germination experiments. The hydrogel exhibited outstanding water absorbency, salt resistance and dry–wet cycling stability, with swelling ratios of 712 g/g in deionized water and 285 g/g in 0.9% NaCl solution, and remained 200 g/g after four dry–wet cycles. It enhanced soil water retention remarkably (over 93% after 72 h). At 0.30% dosage, soil salt content declined from 7.1 g/kg to 1.3 g/kg with desalination efficiency exceeding 80%, owing to porous physical adsorption and chemical chelation toward Na⁺, Ca²⁺ and Mg²⁺, with a binding energy of −136.936 kJ/mol. Pot tests revealed that crop germination rate rose from 19% (blank) to 75% under severe saline–alkali stress. Meanwhile, the hydrogel inhibited nutrient leaching and favored soil-water conservation. This work first incorporated PEGMC monomer into agricultural hydrogels to construct a stable dual crosslinked network, clarifying its synergistic mechanisms for salt fixation and water retention macroscopically and microscopically. It provides a promising functional material and theoretical basis for green, efficient in situ amelioration of dryland saline–alkali soil. Full article

Real-time perception of coal–biomass blending during conveyor-belt transport remains challenging because of local aggregation, particle overlap, and illumination variation. In this study, a laboratory-scale conveyor-belt image dataset covering different coal mass fractions, illumination conditions, and particle sizes was constructed. Whole-image classification, cropped-ROI classification, direct regression, horizontal bounding box (HBB)-based detection, oriented bounding box (OBB)-based detection, and RT-DETR-L detection baselines were compared using YOLO-series and auxiliary models. Coal mass fraction was estimated using a frequency-weighted statistical strategy that converts frame-level predictions into continuous estimates. YOLOv8-cls achieved an average RMSE of 13.98 percentage points (pp), indicating the influence of background interference in whole-image classification. Among HBB models, YOLOv8m achieved the lowest mean RMSE of 6.10 pp but required higher computational cost. Compared with YOLOv8n, YOLOv8n-OBB reduced the average RMSE from 9.02 to 6.90 pp by providing a more compact material-region representation and reducing background redundancy. These results show that OBB representation improves the stability of lightweight models. The proposed method provides a feasible vision-based soft-sensing approach for online trend monitoring of coal–biomass blending under lightweight deployment. Full article

The performance of the cleaning system in crop harvesters directly impacts overall operational efficiency and harvest quality. Against the background of traditional design relying on physical experiments—which is costly and provides limited mechanistic insight—Discrete Element Method (DEM), Computational Fluid Dynamics (CFD), and their coupled simulation (CFD-DEM) have become key means for in-depth study of the cleaning process, capable of revealing the complex interactions between particles and between particles and airflow. With the increasingly widespread and deep application of computer simulation technology in agricultural machinery research and development, it is particularly necessary to systematically review its research progress in cleaning systems. Therefore, this study provides a comprehensive and systematic analysis and summary of the key technologies in cleaning system simulation, aiming to address the current gap in systematic reviews of simulation technology in this field. Compared with previous studies that mostly focus on a single method or a specific crop type, this paper systematically reviews the application of three simulation technologies in cleaning systems of various crop harvesters. First, based on the working principle and core operational challenges of cleaning systems, the necessity of applying simulation technology is clarified. Second, the basic principles, modeling processes, and suitable application scenarios and key points for the cleaning simulation of each method are analyzed. Third, typical cases are reviewed to summarize their key achievements in structural innovation, parameter optimization of cleaning devices, and revealing the mechanisms of material separation. Finally, current bottlenecks in simulation applications are pointed out, and future development directions are outlined, including high-precision multi-field coupling, integration with intelligent algorithms, and the construction of digital twin systems. This study aims to provide systematic theoretical reference and methodological support for the innovative design and performance improvement of cleaning systems. Full article

Horticultural facilities can boost crop yields and quality. However, their structures, costs, and resource efficiency vary significantly. Many facility operators prioritize short-term economic gains at the expense of long-term investments in energy efficiency and environmental management, ultimately leading to increased energy consumption and higher greenhouse gas emissions. A systems-based assessment of tomato production is essential for optimizing resource use. This study integrated emergy analysis (EMA) and life cycle assessment (LCA) to evaluate the sustainability of three tomato production systems: polytunnels, solar greenhouses, and glass greenhouses. The Results demonstrated that polytunnels exhibited the best environmental performance, with the lowest environmental loading ratio (ELR, 19.06) and environmental final index (EFI, 1.62). Solar greenhouses showed the best environmental composite index (ECI), outperforming others in mitigating potential environmental impacts. Glass greenhouses imposed the greatest environmental pressure (ELR, 168.51), primarily due to substantial natural gas consumption and infrastructure investment. Scenario analyses revealed that environmental performance across all systems could be significantly enhanced through shortening transport distance, extending the service life of construction materials, and managing energy use. The maximum reduction potentials for the environmental composite index (ECI)were 23.80% for polytunnels, 18.60% for solar greenhouses, and 19.90% for glass greenhouses. This study confirms that polytunnels are the most environmentally friendly option, and targeted management strategies can effectively steer facility-based agriculture toward a more sustainable trajectory. Full article

The genus Trifolium comprises numerous species that serve as globally important forage and ornamental crops. However, phenotypic difference between species were difficult to define in many cases because of the wide range of diversity caused by primary polymorphism. To effectively identify and differentiate Trifolium species, a total of 5288 candidate EST-SSR molecular markers were developed based on Trifolium repens transcriptome sequencing results, and 132 EST-SSRs that produced clear, reproducible, and highly polymorphic bands were verified after random selection and initial screening. Finally, 202 different bands were amplified by the 28 pairs of SSR primers, and variety identification and DNA fingerprinting were constructed for 16 Trifolium varieties mainly cultivated in China. The polymorphism information index (PIC) ranged from 0.117 to 0.432, with an average of 0.311. Cluster analysis and principal component analysis demonstrated that white clover clustered into a separate group, suggesting a relatively distant genetic relationship with the other 12 Trifolium materials. The DNA fingerprint map of Trifolium species constructed using highly polymorphic markers can effectively distinguish 16 different Trifolium materials. Notably, these markers developed from T. repens show high interspecific transferability, providing a powerful tool for further dissecting genetic diversity within the Trifolium genus, accelerating marker-assisted breeding programs, and reconstructing species domestication trajectories. Full article

Sugar beet (Beta vulgaris L.) is a biennial herbaceous plant belonging to the genus Beta within the family Amaranthaceae. Its root tuber can be used as an effective source for sucrose production. In the pursuit of sustainable development and maximizing the economic value of crops, the full utilization of crop germplasm resources and efficient production is necessary. To better facilitate the collection and utilization of sugar beet germplasm resources, this study used 106 accessions of multigerm sugar beet germplasm provided by the Key Laboratory of Molecular Genetic Breeding for sugar beet as materials. We evaluated the core collections constructed under various strategies using relevant genetic parameters and ultimately established two core collection construction strategies based on morphological and molecular markers. The optimal strategy based on morphological data was “Euclidean distance + Multiple clustering deviation sampling + UPGMA + 25% sampling proportion”, while the optimal strategy based on molecular marker data was “Jaccard distance + Multiple clustering random sampling + UPGMA + 20% sampling proportion”. In addition, representativeness evaluation of the core collection was conducted based on parameters related to both morphology and molecular markers. Principal component analysis (PCA) was utilized for the final determination of the core collection. The results showed that for both the morphological parameters and molecular marker-related parameters, there were no significant differences between the constructed core collection and the original germplasm; the phenotypic distribution frequencies were basically similar. Principal component analysis indicated that the core collection possessed a population structure similar to that of the original germplasm. The constructed core collection had good representativeness. This study, for the first time, proposed a core collection construction approach suitable for sugar beet by integrating morphological and molecular marker methodologies. It aimed to provide a scientific basis for the utilization and development of sugar beet germplasm resources, genetic improvement, and the breeding of new cultivars. Full article

High soil sulfate levels can inhibit crop growth and accelerate concrete infrastructure degradation, highlighting the critical importance of rapid and accurate sulfate content determination. Nevertheless, conventional analytical techniques are laborious and intricate, and delays in processing may result in alterations to the material, owing to oxidation. We recognized the accuracy, reproducibility, and non-invasiveness of mid-infrared (MIR) spectroscopy as a rapid and straightforward technique for soil analysis. In this study, soil samples were collected from two depths (0–20 cm and 20–40 cm) across three regions in China: the arid northwestern region, the cold-temperate northeastern zone, and the subtropical southwestern region. One group was mixed with Na₂SO₄ (a readily soluble salt) at mass fractions ranging from 0.1% to 7%, while the other group was mixed with FeS₂ (a sulfide) at mass fractions ranging from 1% to 70%. This study aimed to develop a mid-infrared spectroscopy-based method for analyzing soluble sulfate and sulfide in soil. Three chemometric methods were evaluated: partial least squares regression (PLSR), principal component regression (PCR), and multivariate linear regression (MLR). Results showed that the MLR model provided superior predictive performance. For the 20–40 cm sodium sulfate-mixed soil from the arid northwestern region, the MLR model exhibited the best performance with an Rp² of 0.9535, an RMSEP of 0.0030, an RPD of 4.96, and an RPIQ of 6.26. For the 20–40 cm iron disulfide-mixed soil from the cold-temperate northeastern region, the MLR model demonstrated superior results with Rp², RMSEP, RPD, and RPIQ values of 0.9590, 0.042, 5.97, and 10.94, respectively. For the 0–20 cm iron disulfide-mixed soil from the subtropical southwestern region, the MLR model achieved the best performance with an Rp² of 0.9848, an RMSEP of 0.0025, an RPD of 14.20, and an RPIQ of 25.48. Despite regional variations in soil properties, this study successfully predicted sulfate and sulfide contents in soils from diverse areas using mid-infrared spectroscopy combined with appropriate chemometric methods. This approach provides reliable technical support for soil sulfate detection and offers significant practical value for soil assessment in both agricultural production and engineering construction. Full article

Carbon-neutral and carbon-negative construction is gaining significant interest in the home building industry. Accordingly, the development of new materials and innovative redesign of the existing materials are on the rise. This paper presents the results of a review study on hempcrete as a new, emerging construction material, which is crop-based and is accordingly expected to provide a highly sustainable construction system. The paper reviews the mixture design, properties and attributes, different methods for its application in construction, building code requirements for construction of hempcrete homes, mechanical and structural properties for home building, and evaluation of the current state of hempcrete application as a non-load-bearing construction material. The paper also reviews the status of developments toward using hempcrete as a load-bearing system. The study shows a snapshot of the methods used for the construction of hempcrete buildings and touches on efforts that are ongoing to increase the compressive strength of hempcrete toward load-bearing applications. Such an increase would depend on different factors such as curing temperature and humidity, binder type and percentage, hemp-to-binder ratio, water-to-binder ratio, and additives. Full article

This work identifies relevant sustainability targets from the UN’s Sustainable Development Goals (SDGs) for main value chains of biobased products, categorized into four dimensions: environment, circularity, social, and economics. Of the 17 Sustainable Development Goals (SDGs), 85 targets were identified as aligning with sustainability criteria for industrial biobased systems. Six sectors with biobased activity were analyzed, chemicals, construction, plastics, textiles, woodworking, and pulp and paper, each represented by 3–5 value chains. These value chains were chosen based on certification availability, production scale in Europe, economic importance, and potential to replace fossil-based products. In total, 25 value chains were assessed qualitatively for their positive, negative, or neutral impact on each selected SDG target, using public data like EU reports, life cycle analyses, and expert insights. The results showed that 43 SDG targets were directly applicable to the value chains, with higher synergies for those using waste as feedstock over primary resources like crops or virgin wood. Overall, advances in technology and holistic approaches are paving the way for biobased solutions to replace resource-intensive, petroleum-derived materials and chemicals. These alternatives offer additional advantages, such as enhanced recyclability, biodegradability, and reduced toxicity, making them promising candidates for sustainable development. This study underscores that technological progress and a comprehensive approach can further advance sustainable biobased solutions in industry and have a relevant positive impact on various SDGs. Full article

The discovery of efficient and stable nanopesticides with improved water solubility and sustained release effects has become particularly important. Pyrimethanil (Pyr) as a low toxicity fungicide of an aniline pyrimidine group is widely used for the prevention and control of gray mold in crops and ornamental plants, however, poor water solubility hinders its further development. Herein, we use a supramolecular self-assembly process to encapsulate a pyrimethanil in a hydroxypropyl-gamma-cyclodextrin (HPγCD) via electrostatic interactions, thereby constructing the inclusion complex nanofibers. The HPγCD as an environmentally friendly carrier material for pesticide delivery is favorable for facilitating the control efficacy, water solubility, and thermostability with Pyr. The diameter of the prepared inclusion nanofiber is 426.6 ± 82.1 nm. Pyr/HPγCD inclusion complex nanofibers could be completely dissolved in water within 3 s. As predicted, the fungicidal activity of Pyr/HPγCD inclusion complex nanofibers is much higher than that of either Pyr, and the EC₅₀ value of Pyr/HPγCD inclusion nanofibers is 0.437 μg/mL, which is about half of that of Pyr (0.840 μg/mL). The inclusion strategy achieved by Pyr and HPγCD is important for improving the safety of nanopesticides. This work provides a versatile insight to promote the development of water-based pesticide dosage forms and reduce pesticide losses in agricultural production. Full article

The areca nut (Areca catechu L.) is a prominent tropical and subtropical crop of economic importance renowned for its significant medicinal value. It is recognized as one of the most prominent components of the four traditional Southern Chinese medicines. However, the lack of comprehensive genetic diversity data and reliable molecular markers has posed challenges in assessing and improving the areca nut germplasm for breeding programs. This study analyzed 196 areca nut materials, employing 40,173 high-quality single-nucleotide polymorphisms (SNPs) to evaluate the genetic relationships among the samples. Population structure analysis identified three distinct groups based on the optimal K-value, with the principal component analysis (PCA) results consistent with the results of population structure division. A phylogenetic tree constructed using the neighbor-joining method revealed clear separations among the samples based on their geographic origins. The nucleotide diversity (π) values ranged from 2.46 × 10⁻⁵ to 5.71 × 10⁻⁵, indicating limited genetic diversity within the areca nut population. The pairwise population differentiation index (Fst) revealed moderate genetic differentiation among the groups. The discovery of these SNPs will be helpful for areca nut conservation and utilization. The results of this study indicate the limited genetic diversity within areca nut germplasm resources, providing insights for management and breeding. Full article

Protected agriculture in Ecuador began in the 1990s and has expanded due to its comparative advantages over open field production. However, there are no statistics on this sector, which limits decision-making. The aim of this research was to provide a baseline of greenhouse agriculture in Imbabura. Sentinel-2 satellite imagery was used to estimate the spatial distribution of plastic-covered surface area in 2016 and 2023. To minimize biases in estimation, manual verification was also conducted. Based on population data, a structured survey was administered to a probabilistic sample of 234 greenhouses. The results highlight the presence of 1958 greenhouses that cover 527 hectares, with an average of 0.26 hectares. The greenhouses were characterized in terms of their design, construction materials and equipment. The main crop under plastic is tomato, with 76.9%, of which the management characteristics and the productive and economic results obtained in 2023 were identified. The findings could inform the formulation of public policies or specific interventions to strengthen protected agriculture in the region; however, support mechanisms are needed to fully exploit its potential. Among these, producer organization could be a viable strategy to address food security challenges in the context of climate change. Full article

Rice is a short-day thermophilic crop that originated from the low latitudes of the tropics and subtropics; it requires high temperatures for growth but is sensitive to low temperatures. Therefore, it is highly important to explore and analyze the molecular mechanism of cold tolerance in rice to expand rice planting areas. Here, we report a phenotypic evaluation based on low-temperature stress in indica rice (R998) and wild rice (GZW) and a comparative transcriptomic study conducted at six time points. After 7 days of low-temperature treatment at 10 °C, R998 exhibited obvious yellowing and greening of the leaves, while GZW exhibited high low-temperature resistance, and the leaves maintained their normal morphology and exhibited no yellowing; GZW has a higher survival rate. Principal component analysis (PCA) and cluster analysis of the RNA-seq data revealed that the difference in low-temperature resistance between the two cultivars was caused mainly by the difference in low-temperature treatment after 6 h. Differential expression analysis revealed 2615 unique differentially expressed genes (DEGs) in the R998 material, 1578 unique DEGs in the GZW material, 1874 unique DEGs between R998 and GZW, and 2699 DEGs that were differentially expressed not only between cultivars but also at different time points in the same material under low-temperature treatment. A total of 15,712 DEGs were detected and were significantly enriched in the phenylalanine metabolism, photosynthesis, plant hormone signal transduction, and starch and sucrose metabolism pathways. These 15,712 DEGs included 1937 genes encoding transcription factors (TFs), of which 10 have been identified with functional validation in previous studies. In addition, a gene regulatory network was constructed via weighted gene correlation network analysis (WGCNA), and 12 key genes related to low-temperature tolerance in rice were identified, including five genes encoding TFs, one of which was identified and verified in previous studies. These results provide a theoretical basis for an in-depth understanding of the molecular mechanism of low-temperature tolerance in rice and provide new genetic resources for the study of low-temperature tolerance in rice. Full article