Elevated nutrient inputs exacerbate the conflict between the advancement of fruit production and environmental sustainability. Quantifying the emission-reduction potential of fruit production systems, predicting environmental impacts, and identifying key orchard management practices are critical to promoting the sustainability of fruit production. However, predictive models for orchard environmental impact are primarily based on machine-learning approaches and fail to adopt an efficiency-oriented perspective to quantify emission reductions in orchards with high yields and high partial factor productivity of nitrogen fertilizer (PFP-N). Therefore, this study adopts life-cycle assessment, a deep-learning predictive model, and a slack-based measure (SBM)-undesirable model to evaluate and forecast the environmental impacts of orchards, which encompasses global warming potential (GWP), reactive nitrogen losses (Nr), acidification potential (AP), and eutrophication potential (EP), while also identifying the mitigation potential of orchards. In addition, local sensitivity analysis reveals the extent to which each input variable affects the model predictions. The results indicated that the emission-reduction potential for the high yield and high PFP-N group was quantified as 53.31%, 52.28%, 50.54%, and 52.65% for GWP, Nr, AP, and EP, respectively. The application amount of nitrogen fertilizer is the largest contributing factor among the four environmental impacts (GWP, Nr, AP and EP). These findings are helpful for assessing and predicting environmental impacts, quantifying emission-reduction potential, and determining the relative importance of agricultural input factors associated with environmental impacts, thereby providing potential theoretical support for promoting sustainable orchard development. Full article

Nutrient surpluses in regions with intensive livestock farming challenge sustainable crop production and have driven interest in alternative fertilization strategies and microbial biostimulants. Although microbial inoculation (MO) has been extensively studied in plant production, its agronomic relevance under field conditions remains controversial due to inconsistent outcomes. To address these inconsistencies, we conducted three-year field trials on two well-fertilized sandy sites in northern Germany. A microbial consortium consisting of Rhizoglomus irregulare, Funneliformis mosseae, Funneliformis caledonium, and Bacillus atrophaeus Abi05 was applied to silage maize (cultivar Amaroc S230) under contrasting fertilization regimes. In two of three years, microbial inoculation increased dry mass yield in the absence of starter fertilization, whereas both a high nutrient input variant (100 kg ha⁻¹ diammonium phosphate, DAP) and a lower nutrient input organo-mineral microgranular fertilizer (25 kg ha⁻¹) suppressed inoculant effects. Notably, yields from plots amended solely with the microbial inoculant reached at least the same level as those obtained with starter fertilization. In the third year, under drought conditions, defined as soil water contents below 10% in the 0–30 cm depth, no positive yield responses to microbial inoculation were observed. Quantitative PCR-based analyses of pre-sowing soils revealed that the abundances of Firmicutes, β-Proteobacteria, and total fungi were associated with yield responses, with Firmicutes and β-Proteobacteria showing negative and fungi showing positive correlations; together, these microbial predictors explained 38% of the variance in inoculant-induced yield response. Our findings demonstrate that soil microbiome characteristics can predict inoculant performance and that microbial inoculation is most effective without starter fertilization and under adequate soil moisture. Full article

Cold-pressed sunflower oil has gained increasing attention for its superior nutritional quality and retention of natural antioxidants compared to refined oils. Its composition and oxidative stability, however, are strongly influenced by both genetic factors and environmental conditions during seed development. Variations in temperature, solar radiation, and humidity can alter the activity of desaturase enzymes and the accumulation of bioactive compounds, thereby determining the sensory and functional quality of the oil. This study provides a comparative assessment of cold-pressed sunflower oils obtained from oil-type hybrids cultivated in Serbia and Argentina, and confectionery hybrids (intended for food use) grown in Serbia, in order to elucidate the combined effects of climatic conditions and genetic diversity on oil quality. Oils from Serbian-grown hybrids exhibited higher oleic acid (30.54–42.72%) and lower linoleic acid contents (46.03–58.44%) compared with those from Argentina, indicating temperature-driven desaturase inhibition. Total tocopherol content ranged from 341.56 to 719.41 mg/kg, while carotenoids and chlorophylls varied between 3.75–17.78 mg/kg and 0.02–1.43 mg/kg, respectively, with elevated pigment accumulation under higher solar irradiance. All oils met Codex Alimentarius standards and showed low peroxide (1.54–7.06 mmol/kg) and acid values (0.40–3.87 mg KOH/g). Principal component analysis differentiated oils according to geographical origin and hybrid type, explaining over 77% of the total variance. These results demonstrate that both genotype and climate decisively modulate fatty acid composition, antioxidant content, and oxidative behavior, shaping the nutritional properties of cold-pressed sunflower oils. Full article

Passiflora germplasm represents an important genetic resource for improving fruit yield and quality in breeding programs targeting semi-arid environments. This study aimed to assess the phenotypic diversity, genetic parameters, and breeding potential of Passiflora accessions conserved in the Passion Fruit Active Germplasm Bank of Embrapa Semiárido. A total of 55 accessions, predominantly Passiflora cincinnata Mast., were evaluated using morphoagronomic descriptors related to plant, flower, and fruit traits. Quantitative data were analyzed using mixed linear models (REML/BLUP) to estimate genetic parameters, and multivariate analyses were applied to characterize phenotypic divergence. Substantial phenotypic variability was observed, particularly for fruit-related traits. Fruit weight ranged from 43.25 to 142.88 g, pulp weight ranged from 7.86 to 51.37 g, and pulp yield ranged from 17.06% to 40.27% among accessions. Broad-sense heritability estimates for key fruit traits were moderate to high, reaching 0.50 for fruit weight, 0.49 for pulp weight, and 0.36 for pulp yield, indicating favorable prospects for selection. Principal Component Analysis explained 66.0% of the total variation in the first two components, with fruit size, pulp-related traits, and seed number contributing most strongly to accession differentiation. Multivariate analyses consistently identified accessions 1 and 16 as superior for fruit weight and pulp yield, whereas accession 55 combined high fruit weight with elevated soluble solid content (up to 14.24 °Brix) but lower pulp yield. Overall, the observed variability highlights the relevance of Passiflora germplasm conserved under semi-arid conditions as a valuable resource for breeding programs focused on fruit yield, quality, and adaptation to water-limited environments. Full article

Salinization stress poses a major environmental factor that adversely affects maize (Zea mays L.) growth and development. Thus, identifying and utilizing alkaline tolerance-related genes in maize is crucial for enhancing resistance to alkaline stress. In this study, a genome-wide association study (GWAS) was conducted to analyze alkali tolerance in seedlings, focusing on biomass-related traits at the seedling stage across a panel of 212 maize inbred lines. The analysis found nine single-nucleotide polymorphism (SNP) loci significantly associated with alkali tolerance during the seedling stage. Within the confidence intervals of these loci, 57 genes with clear functional annotations were identified, among which eight were predicted to be involved in alkali tolerance based on functional annotation and homology analysis. qRT-PCR expression validation of selected candidate genes revealed that the relative expression level of GRMZM2G028089 was similar between in L99 and M-J244-3 lines. In contrast, the expression levels of GRMZM2G071196, GRMZM2G313162 and GRMZM5G883126 were higher in the L99 line compared to M-J244-3, suggesting their potential positive regulatory roles in the response to alkaline stress. These findings provide important theoretical support for the targeted breeding of alkali-resistant maize varieties. Full article

β-agonists are prohibited antibiotics that have raised concerns due to their illegal use in the livestock industry, posing potential toxicity risks to human health. For ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) analysis of β-agonists, effective sample pretreatment is a crucial and challenging process that dictates the overall reliability and sensitivity of the method. Thus, this study developed a reliable method utilizing dispersive solid-phase extraction (d-SPE) with NH₂-UiO-66 as a superior adsorbent, coupled with UPLC-MS/MS, to extract and quantify β-agonists in environmental water, swine urine, and milk. The synthesized NH₂-UiO-66 exhibited outstanding adsorption capacities (146.06–358.00 mg/g) towards the target analytes. The optimized method demonstrated excellent performance: low matrix effects (−13.10–15.30%), wide linearity (0.1–50 μg/L), low limits of detection (0.04–0.09 μg/L), and satisfactory recoveries (81.48–106.67%) with good precision (intra-day RSDs 1.51–6.24%; inter-day RSDs 2.06–10.96%). Adsorption mechanism studies revealed that the extraction process, which followed the Langmuir isotherm and pseudo-second-order kinetic models, was driven primarily by electrostatic interactions, π-π stacking, and hydrogen bonding. Moreover, the material could be reused up to 10 times, with satisfactory recoveries of 81.30% to 116.10%. The proposed NH₂-UiO-66-d-SPE-UPLC-MS/MS protocol is generic and provides a robust and practical solution for monitoring trace β-agonists in animal-derived foods and environmental samples, ensuring food safety and environmental health. Full article

Biochar, produced by pyrolyzing biomass under limited oxygen, can improve soil quality while supporting long-term carbon sequestration. This study compared two wheat-straw biochars (BC) made at 450 °C (BC1) and 600 °C (BC2), with a commercial hardwood biochar produced at 1280 °C (BC3) using lettuce in a sandy, nutrient-poor soil under a carbon capture, utilization, and storage (CCUS) perspective. Higher pyrolysis temperature increased fixed carbon, ash, and alkalinity and reduced volatile matter, indicating greater carbon stability (BC2 > BC1). Germination tests showed good compatibility, with BC1 performing best, likely because moderate temperatures retain more labile organic fractions. In growth-chamber trials (0.75% w/w), biochar boosted lettuce biomass and root development mainly when combined with mineral fertilization, with BC2 (25% and 59%, respectively) and BC3 (18% and 52%, respectively) yielding the strongest gains; unfertilized plants responded little, confirming that biochar is mainly a soil conditioner rather than a nutrient source. Biochar also stimulated soil enzymes linked to C, N, and P cycling and improved leaf chlorophyll, nitrogen status, and antioxidant capacity under fertilization. The nutrient profiles differed by biochar: BC1 increased K and nitrate, while BC2/BC3 lowered nitrate and BC3 enhanced Ca, Mg, and P uptake. Overall, agronomic outcomes depend on feedstock and pyrolysis temperature: mid-temperature biochars enhance productivity and soil biological activity, whereas high-temperature biochars maximize carbon permanence. Full article

A flexible visible/near-infrared (Vis/NIR) sensing system (FVNS) was developed for the non-destructive assessment of almond composition. Almonds from four distinct varieties were measured under non-contact conditions, and the acquired spectra were preprocessed using Savitzky–Golay (S–G) smoothing and standard normal variate (SNV). Based on the spectral data captured by the FVNS, random forest (RF) regression models were established to quantify protein and fat contents. The optimized RF models achieved prediction coefficients of determination (R²_p) of 0.91 for protein and 0.86 for fat, with corresponding residual predictive deviation (RPD) values of 3.32 and 2.67, respectively. These results demonstrate that the FVNS possesses reliable quantitative capability and can accurately capture compositional variations in almonds while maintaining low cost, portability, and real-time wireless operation. Full article

High-resolution yield forecasting is essential for advancing precision agriculture and improving the sustainability of wheat and barley production. While most previous studies focus on field-scale predictions, pixel-level approaches are needed to capture intra-field variability and support site-specific management. This paper evaluates the performance of machine learning models for 10 m resolution yield prediction using multi-temporal Sentinel-2 surface reflectance data across seven major cereal-producing regions in Spain. Yield monitor data from winter wheat and barley fields collected over five growing seasons (2020–2024) were combined with spectral bands and vegetation indices. Random Forest (RF) and XGBoost (XGB) models were trained at five phenological stages expressed as days before harvest (DBH) and validated using both internal (2020–2023) and independent external (2024) datasets. Model accuracy increased as harvest approached. In external validation, RF achieved the best performance for wheat (R² = 0.77; RMSE ≈ 697 kg · ha⁻¹), while XGB performed best for barley (R² = 0.86; RMSE ≈ 744 kg · ha⁻¹). Visible, red-edge, and SWIR bands were the most informative predictors, especially during grain filling and senescence. Results demonstrate the potential of multi-temporal Sentinel-2 data and machine learning for accurate, transferable, pixel-level yield forecasting in Mediterranean cereal systems. Full article

Sorting defective cocoons is a critical procedure in the silk reeling industry to ensure the quality of raw silk products. Currently, this process relies heavily on manual inspection, which is labor-intensive, subjective, and inefficient. While automated sorting based on machine vision offers a promising alternative, existing object detection algorithms struggle to balance accuracy and computational complexity, particularly when detecting tiny surface defects or distinguishing morphologically similar cocoons in dense scenarios. To address these challenges, this paper proposes an efficient silkworm cocoon defect detection model named LMD-YOLO, based on the YOLOv10 architecture. In this model, we introduce three key improvements to enhance feature extraction and multi-scale perception. First, we integrate a Large Separable Kernel Attention (LSKA) module into the C2f structure (C2f-LSKA) of the backbone. This design decomposes large kernels to capture global shape features with minimal computational cost, effectively distinguishing double cocoons from normal ones. Second, we replace standard upsampling with a DySample module in the neck, which utilizes dynamic point sampling to recover fine-grained texture details of tiny defects like surface stains. Third, a Multi-Scale Dilated Attention (MSDA) mechanism is embedded before the detection heads to aggregate semantic information across different scales, improving robustness against background interference. YOLOv10 was selected as the baseline due to its NMS-free characteristic, which mitigates the latency caused by post-processing in high-speed sorting tasks. Evaluations on a self-constructed multi-category dataset indicate that LMD-YOLO surpasses established detectors, including YOLOv8n and Faster R-CNN. Relative to the YOLOv10n baseline, our method improves mAP@0.5 by 3.11%, achieving 94.46%. Notably, Precision and Recall are increased by 3.50% and 2.97%, reaching 89.98% and 93.61%, respectively. With a compact size of 2.68 M parameters and an inference speed of 115 FPS, the proposed model offers a practical trade-off between accuracy and latency for real-time cocoon defect detection. Full article

Cotton is one of the most significant economic crops cultivated worldwide. Cottonseed is a strategic reservoir of high-quality plant protein and an underexploited resource for the food and feed industries. To quantify nutritional diversity and identify superior germplasm, we evaluated 312 upland cotton (Gossypium hirsutum L.) accessions over two consecutive growing seasons and characterized 30 agronomic and nutritional traits. Protein content varied widely (29.6–48.8%), with a coefficient of variation of 7.5–11.7% and a two-year mean of 37.0%. Glutamic acid (Glu; 154.0 mg/g) and aspartic acid (Asp; 90.7 mg/g) were the most abundant amino acids, and lysine and arginine were relatively high among essential amino acids. Correlation analysis based on genotype best linear unbiased estimates (BLUEs) showed that most nutritional traits were positively or neutrally associated with key yield-related traits, particularly lint percentage (LP) (e.g., protein vs. LP: r = 0.18, p < 0.01), indicating the feasibility of simultaneous improvement in seed nutritional quality and lint yield potential. Using 29 core traits with complete two-year data, we developed an integrated evaluation framework combining principal component analysis (PCA), grey relational analysis (GRA), TOPSIS, and the analytic hierarchy process (AHP) to rank accessions comprehensively. This framework identified 10 elite germplasm lines with high protein content and favorable yield potential, exemplified by “Xinluzhong 34” (Rank 1; phenotypic comprehensive value, P_i = 0.733). These results provide a quantitative foundation for value-added cottonseed utilization and support breeding strategies aimed at developing cultivars with both high yield and enhanced nutritional quality. Full article

Iron ore tailings have been shown to promote the formation of soil aggregate cementing agents through weathering, thereby influencing soil aggregate formation in reclaimed land. However, their mechanism of action under different reclamation methods remains unclear. This study established a field station in the semi-arid region of Northern China to investigate three typical iron ore tailing reclamation methods, including topsoil blending type (DT), sublayer moisture conservation type (JT), and thick-layer tailings type (FT), with adjacent farmland as the control (CK). The analysis of soil organic carbon (SOC) components, soil inorganic carbon (SIC), iron/aluminum oxides, and aggregate composition and stability in the reclaimed soils revealed the evolution patterns of cementing materials and the potential mechanisms driving aggregate formation. The results indicate that the reclamation process promotes the weathering of tailings, with a significant increase in free iron oxide (Fed) content ranging from 19.09% to 41.93%. Iron oxides released from iron ore tailings influenced the reclaimed topsoil through plant litter return processes, resulting in a significantly higher amorphous iron oxide (Feo) content compared to CK. Additionally, the content of crystalline aluminum oxide (Alc) in the DT topsoil showed a significant increase, reaching 2.82 g/kg. The variation in organic and inorganic cementing agents significantly influences aggregate composition and stability, with soil particulate organic carbon (POC), crystalline iron oxide (Fec), Alc, and amorphous aluminum oxide (Alo) identified as the primary agents affecting aggregate formation (p < 0.05). After five years of reclamation, the proportion of DT macroaggregates (>0.25 mm) increased to 42.10%, and both the mean weight diameter (MWD) and the geometric mean diameter (GMD) increased significantly to 2.21 mm and 0.43 mm, respectively. In contrast, JT macroaggregates and microaggregates (0.053–0.25 mm) decreased to 26.88% and 29.01%, respectively, and aggregate stability significantly declined. FT macroaggregates and their stability showed no significant difference compared to CK. The study shows that after years of reclamation, both DT and FT reclamation methods have reached normal farmland levels in terms of aggregate formation and stability, making them practical and valuable reclamation solutions. Full article

Rapid and uniform seedling establishment is critical for the productivity of direct-seeded rice, particularly in late-season cropping systems where sowing frequently coincides with high-temperature stress. Current seed quality assessment relies predominantly on the Standard Germination Test (SGT); however, this method, conducted under optimal conditions, often fails to predict field performance in thermally stressful environments. To resolve this discrepancy, this study established a High-Temperature Germination (HTG) protocol optimized specifically for late-season rice. Twenty-three diverse rice genotypes—comprising conventional japonica, indica-japonica hybrids, and indica hybrids—were evaluated using SGT and HTG assays at 35 °C, 38 °C, and 41 °C, incorporating a pre-treatment with trichloroisocyanuric acid (TCCA) to standardize initial seed conditions. Validation was conducted through field trials at two distinct locations in Zhejiang, China. The results demonstrated that while SGT indicated high viability (>85%) for most varieties, it exhibited a poor correlation with field emergence (r < 0.31). In contrast, HTG tests conducted at 38 °C and 41 °C showed reliable predictive validity, yielding highly significant correlations with field establishment (r > 0.70, p < 0.001). Significant genotypic variation was observed: hybrid varieties displayed superior thermotolerance and stable germination even at 41 °C, whereas conventional japonica varieties exhibited marked sensitivity to temperatures exceeding 35 °C. These findings highlight the potential of the HTG assay (specifically at 38 °C or 41 °C) as an effective, cost-efficient, and rapid screening tool. By accurately simulating the acute thermal stress of the sowing-to-emergence window, this method facilitates the identification of climate-resilient germplasm and supports reliable stand establishment in direct-seeded rice production. Full article

Downy mildew (Plasmopara viticola) poses a major and recurring threat to Greek viticulture, yet existing point-based forecasting models require in-vineyard stations, limiting scalability in fragmented landscapes. This study introduces a spatially explicit model (MeteoGrape) using one fully equipped reference meteorological station plus eight distributed sensors across an 85 km² area in Kavala, Greece. The model is structured in three phases. In Phase A, a single reference station was paired with eight low-cost distributed sensors to reconstruct hourly temperature and relative humidity data through regression correction and radial basis function interpolation, generating a 342-cell grid at 0.005° resolution. During Phases B and C, deterministic epidemiological rules were applied to simulate oospore development, with accumulated degree-hours and humidity exposure converted into spatial risk classifications. Cross-validation (leave-one-sensor-out) confirms meteorological reliability. The model captured an elevated risk period beginning on 16 May, preceding the regional advisory bulletin (23 May), and mapped the spatial distribution of accumulated risk through late May. Validation supports temporal consistency at the regional scale, while fine-scale spatial accuracy is identified as a subject for future field-based evaluation. The framework demonstrates the feasibility of extending established point-based disease models into spatially explicit risk maps under limited meteorological infrastructure. Full article