Search Results (1,290)

In order to meet the dual requirements of low-energy heating and flexible operation, a comprehensive heating system with multi-mode and wide-load capabilities was constructed, incorporating a heat pump, a back-pressure turbine, and two 350 MW coal-fired condensing units. Based on the heat transfer characteristics of this system, the simulation model of this comprehensive thermal system was constructed through a commercial software (EBSILON). A dynamic coal consumption prediction method based on the non-equilibrium state parameters was first proposed, which was primarily designed for system operation optimization. Subsequently, the converted load and load change rate were integrated into the dynamic correction model to refine prediction accuracy. The results showed that while basic coal consumption primarily correlates with heat load and electricity load, dynamic coal consumption is influenced by both the converted load and the load change rate. Based on this, the three-dimensional surface plot of converted load, load charge rate, and dynamic coal consumption offset coefficient was calculated. Then, the accuracy of the prediction model was verified by the variable working condition parameter group, and its reliability was confirmed. Further, by developing online software, theoretical guidance for industrial production was realized. In a heating season case study, it was demonstrated the prediction method can effectively reflect the dynamic parameter deviation in the system, with the annual coal saving being able to reach 841.5 tons. It is expected to provide theoretical guidance for the research on multi-heat sources heating distribution and operation parameter optimization. Full article

The high-latitude cold regions of northeastern China present scarce thermal resources, exhibit a short frost-free period, and lack high-yielding maize (Zea mays L.) varieties suitable for dense planting. These factors have long constrained the realization of maize yield potential under dense planting conditions. This study investigated the effects of intercropping maize varieties with different growth periods on the photosynthetic performance, yield formation, and interspecific competition. The long-season varieties Zhengdan958 (ZD958) and Xianyu335 (XY335), which are representative of the region, were intercropped with the shorter-season variety Yinongyu10 (YNY10), six intercropping row ratios (6:6, 4:4, 2:2, 1:1, 0:1, and 1:0) were set, and monoculture plots (0:1 and 1:0) were used as the controls. The results indicated that as the row ratio decreased in the intercropped plots, the leaf area index, relative leaf chlorophyll content, photosynthetic rate, stomatal conductance, and transpiration rate increased while the intercellular CO₂ concentration gradually decreased compared with those in the monoculture plots. Simultaneously, dry matter accumulation, allocation, transport efficiency, 100-kernel weight, number of kernels per ear, and grain yield progressively increased, reaching maximum values at a 1:1 intercropping row ratio. Conversely, YNY10 in the intercropped plots exhibited opposite trends in these parameters. The land equivalent ratios for all intercropped row ratios exceeded 1. During the 2023–2024 growing season, the composite population grain yield was significantly higher (p < 0.05) at an intercropping row ratio of 1:1 for ZD958 (4.11–4.26%) and XY335 (3.54–3.65%) compared with the monoculture treatments, demonstrating the strong yield advantage of intercropping. Furthermore, in the intercropping systems, ZD958 and XY335 exhibited positive aggressivity and a competitive ratio greater than 1, thus showing stronger competitive ability than YNY10. Moreover, the increased grain yield of ZD958 and XY335 effectively compensated for the ecological disadvantages of YNY10, thereby leveraging the synergistic effects of close planting and intercropping patterns to promote improvements in maize composite population productivity. Full article

Urban ecosystems are structurally and functionally distinct from their natural counterparts, with anthropogenic management potentially altering fundamental ecological processes such as seasonal community dynamics and impairing their sustainability. However, the mechanisms through which management filters plant diversity across seasons remain poorly understood. This study tested the hypothesis that management acts as an abiotic filter, dampening seasonal community variations and increasing biotic homogenization in urban green spaces. In this respect, through an intensive, multi-seasonal case study comparing two Mediterranean urban green spaces under contrasting management regimes, we analysed plant communities across 120 plots over four seasons. Results reveal a contingency cascade under management: while the species composition remains relatively stable (+26% variability, p < 0.001), the demographic success becomes more contingent (+41%, p < 0.001), and the ecological dominance becomes highly stochastic (+90%, p < 0.001). This hierarchy demonstrates that management primarily randomizes which species achieve dominance, in terms of biomass and cover, from a pool of disturbance-tolerant generalists. A 260% increase in alien and cosmopolitan species and persistent niche pre-emption dominance–diversity patterns also indicate biotic homogenization driven by management filters (mowing, trampling, irrigation, and fertilization) that favors species resistant to mechanical stresses and induces a breakdown of deterministic community assembly. These processes create spatially and temporally variable assemblages of functionally similar species, explaining both high structural variability and persistent functional redundancy. Conversely, seasonally structured, niche-based assemblies with clear dominance–diversity progressions are observed in the unmanaged area. Overall, findings demonstrate that an intensive management homogenizes urban plant communities by overriding natural seasonal filters and increasing stochasticity. The study provides a mechanistic basis for sustainable urban green space management, indicating that reduced intervention can help preserve the seasonal dynamics crucial for sustaining biodiversity and ecosystem functioning. Full article

The accurate monitoring of crop water status is critical for optimizing irrigation strategies in winter wheat. Compared with satellite remote sensing, unmanned aerial vehicle (UAV) technology offers superior spatial resolution, temporal flexibility, and controllable data acquisition, making it an ideal choice for the small-scale monitoring of crop water status. During 2023–2025, field experiments were conducted to predict crop water status using UAV images in the North China Plain (NCP). Thirteen vegetation indices were calculated and their correlations with observed crop water content (CWC) and equivalent water thickness (EWT) were analyzed. Four machine learning (ML) models, namely, random forest (RF), decision tree (DT), LightGBM, and CatBoost, were evaluated for their inversion accuracy with regard to CWC and EWT in the 2024–2025 growing season of winter wheat. The results show that the ratio vegetation index (RVI, NIR/R) exhibited the strongest correlation with CWC (R = 0.97) during critical growth stages. Among the ML models, CatBoost demonstrated superior performance, achieving R² values of 0.992 (CWC) and 0.962 (EWT) in training datasets, with corresponding RMSE values of 0.012% and 0.1907 g cm⁻², respectively. The model maintained robust performance in testing (R² = 0.893 for CWC, and R² = 0.961 for EWT), outperforming conventional approaches like RF and DT. High-resolution (5 cm) inversion maps successfully identified spatial variability in crop water status across experimental plots. The CatBoost-RVI framework proved particularly effective during the booting and flowering stages, providing reliable references for precision irrigation management in the NCP. Full article

Apple scab poses a significant threat to wild apple orchards in the Ili region of Xinjiang, yet the pathogen responsible and its disease dynamics remain poorly understood. This study aimed to identify the causal agent of apple scab in wild apples and elucidate its disease development pattern to support effective monitoring and control strategies. Field surveys were conducted regularly from 2023 to 2025 in fixed plots and sample trees of Malus sieversii. A total of 29 isolates were obtained from diseased fruits collected in Xinyuan and Huocheng counties using tissue isolation and single-spore purification. Pathogenicity was confirmed via Koch’s postulates, and the pathogen was identified based on morphological and molecular characteristics. Scab symptoms first appeared on leaves in late April (during leaf expansion, disease index 0.34) and on fruits in early June (during fruit enlargement, disease index 0.57). The disease index peaked in late August (47.24 on leaves; 22.51 on fruits), followed by fruit drop at month-end and leaf abscission in late September. The pathogen overwintered mainly in remaining or fallen diseased leaves (isolation rate 17.71%), serving as the primary source of initial infection in the following growing season. The pathogen causing apple scab in Xinjiang wild apple orchards was identified as Venturia inaequalis. Overwintered infected leaves were confirmed as the key primary inoculum source. These findings clarify the taxonomic identity of the pathogen and its epidemic pattern, providing a theoretical basis for disease management. Full article

Agricultural soil health and quality centre around the ability of the soil to cycle nutrients to growing crops. However, soil biological properties focusing on microorganisms and their contribution to soil health are also important. This study was established at Syferkuil and Ofcolaco to determine the effect of cover crop, combined nano Zn and Cu, and nitrogen fertiliser on soil biological properties. Sunn hemp was planted, slashed, and incorporated into the soil, followed by winter canola in a split split-plot design with sixteen treatments. Key factors analysed after harvest included bacterial and fungal populations, active carbon, microbial activity measured by Fluorescein Diacetate (FDA), organic matter, urease, and pH. Statistical analysis was conducted using JASP 0.19.3. Cover crop, nano Zn and Cu, and nitrogen fertiliser enhanced bacterial populations, active carbon, urease, organic matter, and pH at Syferkuil, most particularly in 2023, while 2024 showed minor improvements. Ofcolaco showed improvements in fungal populations, organic matter and urease in 2023, whereas 2024 exhibited marginal changes. Nitrogen fertilisation increased POxC, ranging from 10% to 22% and urease at 31% to 111% in both locations, although this varied across application rates. Treatment interactions showed improvements in some of the measured parameters but varied across seasons and locations. In conclusion, sunn hemp cover crop, combined nano zinc and copper and nitrogen fertiliser have the potential to enhance soil microbial activity through the application of 60 and 120 kgN ha⁻¹, thus reducing heavy inputs of synthetic fertilisers in canola production. Full article

Studying the effects of grazing pressure on species composition, beta diversity and yields is important for conservation purposes as well as for grassland management. The case study area in Hortobágy, which is one of the largest continuous grassland areas in Europe, has been managed for centuries by grazing of Hungarian grey cattle. The effect of grazing pressure was investigated in terms of distance from the livestock enclosure (50 m, 250 m, 500 m, 1000 m, and 1700 m) and in an ungrazed control area on dry and mesic alkaline grasslands in spring and autumn of 2024. In both types of grasslands at each distance, species composition and mean plant height were recorded in six 4 × 4 m plots. Overall, in both seasons the control areas were the poorest in terms of species richness. Among the grazed areas in both grassland types the ones at 1700 m distance had the lowest number of species. The species richness of mesic grassland decreased linearly with distance. The dry grassland showed a polynomial trend and was more species-rich at all distances than the mesic grassland. Green yield was the highest in the dry grassland at 250 m in spring and at 50 m in autumn, while in the mesic grassland it was highest at 1700 m in spring and between 500 and 1700 m in autumn. Forage quality in dry grassland was lowest at 50 m and highest between 500 and 1000 m. In mesic grassland, this parameter was equalized at all distances. The highest Simpson diversity was found at a distance of 500–1000 m from the livestock enclosure in both types. It is advisable to evaluate separately the spring and autumn characteristics of the alkaline grasslands, as there may be significant differences between them. Overall, it can be concluded that alkaline dry grasslands are particularly suitable for grazing because of their species composition and their good tolerance to grazing. Alkaline mesic grasslands are poorer in species and more sensitive to grazing; consequently, mowing or mixed utilization should be considered. Full article

In semi-arid regions of Morocco, where the majority of water withdrawals are devoted to irrigation, optimizing irrigation practices in agriculture is a national priority in the face of recurring droughts and growing pressure on groundwater resources. However, the hydrological impacts of different drip-irrigation systems in the soil–plant–atmosphere continuum remain insufficiently understood. We monitored the stable isotope composition (δ²H, δ¹⁸O) across the two agricultural plots in Marrakech (Morocco) with surface drip and subsurface drip irrigation treatments for a complete hydrologic year (June 2022 to June 2023). Weekly to daily samples of rainfall, irrigation water, groundwater, and soil at various depths (5–50 cm) were sampled, and water from branch xylem was extracted using the cryogenic vacuum distillation method. We found that the subsurface irrigation treatment, which delivered water directly to the root zone, maintained narrow isotopic ranges in water of soils beyond 30 cm, as well as in branch xylem and leaf water. By contrast, surface irrigation treatment plots showed pronounced evaporative isotopic enrichment: summer topsoil water δ¹⁸O peaked at −1.1‰ (vs. −8.7‰ in subsurface irrigation treatment), and leaf water reached +13‰ (vs. +8‰ in subsurface). Despite this larger isotopic heterogeneity in surface irrigation site, branch xylem water δ¹⁸O remained within −6 to 2.5‰ across all soil depth, similar to subsurface irrigation treatment, which ranged between −5 and 0‰. This suggests that olive roots accessed soil water uniformly from the upper 50 cm under both irrigation treatments. Seasonal xylem isotopic enrichment in spring and midsummer mirrored shifts towards shallow, evaporatively altered soil water under surface irrigation, but not under the subsurface. The results suggest that subsurface drip irrigation can significantly improve drought resilience and water-use efficiency in the expanding olive sector of the Maghreb, while continuous isotope monitoring serves as a practical approach to enhance sustainable and adaptive water management in water-limited regions. Full article

Soil erosion is a major threat to land productivity and environmental sustainability in Mediterranean regions, where sloping terrain, intense seasonal rainfall, and traditional agricultural practices accelerate soil loss. Olive orchards, which dominate much of the Mediterranean landscape, are particularly vulnerable. As climate change increases the frequency of extreme weather events, understanding and controlling erosion becomes even more critical. This study investigates soil erosion dynamics in a representative olive-growing watershed in Messenia, Greece, by combining field monitoring with erosion modeling using the Revised Universal Soil Loss Equation (RUSLE). A field experiment was carried out during the 2024–2025 wet season, using runoff plots installed on a 16% slope to directly measure sediment loss from natural rainfall events. The observed erosion data served as a basis for calibrating a GIS-based RUSLE model applied across the 60 km² watershed. Model predictions showed strong agreement with field measurements, with estimated soil loss closely matching the observed seasonal total (~0.6 t/ha). This consistency demonstrates the reliability of the RUSLE model when supported by localized data. The spatial analysis further revealed that erosion risk varies widely across the landscape, with steep, poorly vegetated areas being most at risk. The results highlight the importance of local field measurements for improving model accuracy and guiding sustainable land management. Continuous monitoring and targeted erosion control strategies are essential to protect soil resources, maintain agricultural productivity, and reduce downstream environmental impacts under increasing climate pressures. Full article

Pineapple translucency is a major physiological disorder in ‘Tainong 17’ (Golden Diamond) that severely impairs fruit quality, storability, and market value, yet its physiological basis remains poorly understood. To clarify the underlying mechanisms, we conducted two seasons of field surveys across 24 plots in eleven pineapple orchards in Hainan, China, comparing translucent and healthy fruits in terms of plant growth, nutrient status, fruit quality, cell wall composition, and soil properties. Our results showed that translucency significantly reduced fruit quality, with soluble solids and ascorbic acid contents decreasing by 9.7% and 16.3%, respectively. Translucent plants exhibited markedly increased biomass, whereas fruit dry matter was reduced by 21.6%. In addition, affected plants accumulated 40–70% more nitrogen in leaves, stems, and fruits, accompanied by 23% and 14% reductions in abscisic acid concentrations in leaves and fruits, respectively. Calcium and boron allocation to fruits was impaired, with fruit Ca and B contents decreasing by 25.1% and 50.4%, respectively, despite increased levels in vegetative organs. These nutrient imbalances coincided with a 16.4% decrease in protopectin, a 5.3% decrease in cellulose, and a 15.5% increase in soluble pectin, indicating cell-wall loosening. Collectively, our findings demonstrate that excessive nitrogen input disrupts carbon–nitrogen metabolism and ABA signaling, elevates fruit N/Ca ratios, and accelerates cell-wall remodeling, thereby predisposing fruits to translucency, particularly under humid or rainy conditions. Full article

In cool temperate regions, soil respiration (Rs) data collected during the cold season is limited due to freezing and snow. This leads to a lack of understanding of Rs characteristics during the cold season and for ecosystems with long winters, it can significantly impact the annual carbon flux estimation. In this study, Rs data were collected from temperate deciduous forests to understand the characteristics of Rs values in the cold temperature season. To reflect spatial variation in Rs, five points were selected with different levels of litter layer development, ranging from Chamber 1 (almost no litter) to Chamber 5 (thick litter). Rs, air temperature (Ta) and rainfall, soil temperature (Ts) and soil moisture content (SMC) were collected every 30 min at each measurement point. As the litter layer developed, Ts tended to increase, but SMC tended to decrease, revealing that the degree of litter layer development had a clear effect on Ts and SMC. Rs showed a relatively high exponential correlation with Ts. However, the Rs−SMC functional relationship exhibited no correlation. Therefore, while the Ts-Rs functional equation can be used in the Rs calculator during the cold season, the SMC-Rs function would be suitable for use. Also, these deferent litter layers, TS, and SMC affected the Rs. The total Rs during the measurement period was various from 0.60 t C ha⁻¹ for a thin litter layer to 1.88 t C ha⁻¹ for a thick layer. This range of values may be appropriate for estimating Rs during the cold season in temperate regions. Also, the average across all plots was 6.05, ranging from 4.93 in no litter to 8.23 in thick litter layer. Full article

Enhanced Rock Weathering (ERW) is a promising carbon dioxide removal (CDR) strategy that accelerates mineral dissolution, sequestering atmospheric CO₂ while improving soil health. This study builds on prior applications of soil calcimetry by investigating its ability to resolve short-term carbonate fluxes and rainfall-modulated weathering dynamics in wollastonite-amended croplands. Conducted over a single growing season (May–October 2024) in temperate row-crop fields near Port Colborne, Ontario—characterized by fibric mesisol soils (Histosols, FAO-WRB)—this study tests whether calcimetry can distinguish between dissolution and precipitation phases and serve as a proxy for weathering flux within the upper soil horizon, under the assumption that rapid pedogenic carbonate cycling dominates alkalinity retention in this soil–mineral system. Monthly measurements of soil pH (Milli-Q and CaCl₂) and calcium carbonate equivalent (CCE) were conducted across 10 plots, totaling 180 composite samples. Results show significant alkalinization (p < 0.001), with average pH increases of ~+1.0 unit in both Milli-Q and CaCl₂ extracts over the timeline. In contrast, CCE values showed high spatiotemporal variability (−2.5 to +6.4%) without consistent seasonal trends. The calcimetry-derived weathering proxy, log (Σ ΔCCE/Δt), correlated positively with pH (r = 0.652), capturing net carbonate accumulation, while the kinetic dissolution rate model correlated strongly and negatively with pH (r ≈ −1), reflecting acid-promoted dissolution. This divergence confirms that the two metrics capture complementary stages of the weathering–precipitation continuum. Rainfall strongly modulated short-term carbonate formation, with cumulative precipitation over the previous 7–10 days enhancing formation rates up to a saturation point (~30 mm), beyond which additional rainfall yielded diminishing returns. In contrast, dissolution fluxes remained largely independent of rainfall. These results highlight calcimetry as a direct, scalable, and dynamic tool not only for monitoring solid-phase carbonate formation, but also for inferring carbonate migration and dissolution dynamics. In systems dominated by rapid pedogenic carbonate cycling, this approach captures the majority of alkalinity fluxes, offering a conservative yet comprehensive proxy for CO₂ sequestration. Full article

The integration of machine learning (ML) techniques with unmanned aerial vehicle (UAV) imagery holds strong potential for improving yield prediction in agriculture. However, few studies have combined biophysical field variables with UAV-derived spectral data, particularly under conditions of limited sample size. This study evaluated the performance of different ML algorithms in predicting Arabica coffee (Coffea arabica) yield using field-based biophysical measurements and spectral variables extracted from multispectral UAV imagery. The research was conducted over two crop seasons (2020/2021 and 2021/2022) in a 1.2-hectare experimental plot in southeastern Brazil. Three modeling scenarios were tested with Random Forest, Gradient Boosting, K-Nearest Neighbors, Multilayer Perceptron, and Decision Tree algorithms, using Leave-One-Out cross-validation. Results varied considerably across seasons and scenarios. KNN performed best with raw data, while Gradient Boosting was more stable after variable selection and synthetic data augmentation with SMOTE. Nevertheless, limitations such as small sample size, seasonal variability, and overfitting, particularly with synthetic data, affected overall performance. Despite these challenges, this study demonstrates that integrating UAV-derived spectral data with ML can support yield estimation, especially when variable selection and phenological context are carefully addressed. Full article

Hungary faces increasing water challenges, including frequent droughts and a growing dependence on irrigation, which necessitate alternative water sources for agriculture. This study evaluated the use of saline aquaculture effluent—characterized by elevated sodium (Na⁺) and chloride (Cl⁻) concentrations—as an irrigation resource for forage sorghum (Sorghum bicolor L.) over four consecutive growing seasons. Three hybrids (‘GK Áron’, ‘GK Balázs’, and ‘GK Erik’) were tested under five irrigation regimes, including freshwater and aquaculture effluent applied via drip irrigation at weekly doses of 30 mm and 45 mm, alongside a non-irrigated control. Effluent irrigation at 30 mm weekly increased biomass yield by up to 61% and enhanced nitrogen uptake by 22% compared to the control. Soil electrical conductivity (EC) values remained below 475 µS/cm, with effluent treatments showing lower EC than non-irrigated plots. The effluent water also supported the recycling of nutrients, especially nitrogen and phosphorus. Unlike conventional saline water, aquaculture effluent contains organic compounds and microbial activity that may improve nutrient mobilization and uptake. Our results highlight how we can reuse aquaculture wastewater in irrigated crop production. The results demonstrate that moderate effluent irrigation (30 mm/week) can optimize crop water use while maintaining soil health, offering a viable strategy for forage sorghum production in water-limited environments. Full article

Soybean (Glycine max (L.) Merr.) is an essential legume crop in Thailand, valued for its high protein content and economic significance. However, weed competition can reduce yields by up to 82% if not managed effectively. This study evaluates the efficacy of post-emergence herbicides for weed control and their impact on soybean yield. A field experiment was conducted during the 2023 rainy and 2024/2025 dry seasons at Khon Kaen University using a split-plot design with four replications. Weed management treatments included hand weeding, an untreated control, and three herbicides, fluazifop-P-butyl + fomesafen, clethodim + fomesafen, and quizalofop-P-tefuryl + fomesafen, applied to two soybean varieties (Morkhor60 and CM60). Quizalofop-P-tefuryl + fomesafen was found to be the most effective herbicide, achieving 87.66% weed control efficiency (WCE) in the dry season and 72.43% in the rainy season. Hand weeding produced the highest yield (1324.00 kg ha⁻¹), followed by quizalofop-P-tefuryl + fomesafen (1148.90 kg ha⁻¹). Morkhor60 outperformed CM60 in yield and growth performance. These findings highlight the importance of selecting suitable herbicide treatments to optimize weed control and enhance soybean productivity under different seasonal conditions. Full article