Search Results (581)

Drought stress is a major environmental constraint that significantly reduces wheat productivity worldwide. In this study, seventeen wheat genotypes were evaluated under well-watered and drought-stressed conditions across two consecutive years (2023–2024) in a controlled greenhouse experiment. Twenty morphological and agronomic traits were recorded, and their responses to prolonged water limitation were assessed using multivariate statistical methods, including three-way ANOVA, principal component analysis (PCA), and cluster analysis. Drought stress significantly decreased all traits except the harvest index (HI), with the most severe reductions observed in traits related to secondary spikes (e.g., grain weight reduced by 95%). The ANOVA results confirmed significant genotype × treatment (G × T) interactions for key agronomic traits, with the strongest effect observed for total grain weight (F = 7064.30, p < 0.001). A PCA reduced the 20 original variables to five principal components, explaining 87.2% of the total variance. These components reflected distinct trait groups associated with productivity, spike architecture, and development in phenology. Cluster analysis based on PCA scores grouped genotypes into three clusters with contrasting drought response profiles. A yield-based evaluation confirmed the cluster structure, distinguishing genotypes with a stable performance (average yield loss ~58%) from highly sensitive ones (~70% loss). Overall, the findings demonstrate that drought tolerance in wheat is governed by complex trait interactions. Integrating a trait-based multivariate analysis with a yield stability assessment enables the identification of genotypes with superior adaptation to water-limited environments, providing an excellent genotype background for future breeding efforts. Full article

According to the United Nations Environment Programme reports, buildings are responsible for nearly 40% of energy-related emissions; therefore, energy-optimized building design is crucial to reduce the reliance on non-renewable energy sources as well as greenhouse gas emissions. The OECD reports indicate the use of Building Information Modelling (BIM) as one of the effective strategies for decarbonization of buildings, since a 3D digital representation of both physical and functional characteristics of a building can help to design a more efficient infrastructure. An efficient integration of solar energy in building design can be vital for the enhancement of energy performance in terms of heating, cooling, and lighting demand. This paper presents results of an analysis of how factors related to the use of daylight, such as automatic control of artificial lighting, external shading, or the visual absorptance of internal surfaces, influence the energy efficiency within an example room in two different climatic zones. The simulation was conducted using Design Builder software, with predefined occupancy schedules and internal heat gains, and standard EPW weather files for Warsaw and Genua climate zones. The study indicates that for the examined room, when no automatic sunshades or a lighting control system is utilized, most of the final energy demand is for cooling purposes (45–54%), followed by lighting (42–43%), with only 3–12% for heating purposes. The introduction of sunshades and/or the use of daylight allowed for a reduction of the total demand by up to half. Moreover, it was pointed out that often neglected factors, like the colour of the internal surfaces, can have a significant effect on the final energy consumption. In variants with light interior, the total energy consumption was lower by about 3–4% of the baseline demand, compared to their corresponding ones with dark surfaces. These results are consistent with previous studies on daylighting strategies and highlight the importance of considering both visual and thermal impacts when evaluating energy performance. Similarly, possible side effects of certain actions were highlighted, such as an increase in heat demand resulting from a reduced need for artificial lighting. The results of the analysis highlight the potential of a simulation-based design approach in optimizing daylight use, contributing to the broader goals of building decarbonization. Full article

The integration of digital twin technology with robotic automation holds significant promise for advancing sustainable horticulture in controlled environment agriculture. This article presents DigiHortiRobot, a novel AI-driven digital twin architecture tailored for hydroponic greenhouse systems. The proposed framework integrates real-time sensing, predictive modeling, task planning, and dual-arm robotic execution within a modular, IoT-enabled infrastructure. DigiHortiRobot is structured into three progressive implementation phases: (i) monitoring and data acquisition through a multimodal perception system; (ii) decision support and virtual simulation for scenario analysis and intervention planning; and (iii) autonomous execution with feedback-based model refinement. The Physical Layer encompasses crops, infrastructure, and a mobile dual-arm robot; the virtual layer incorporates semantic modeling and simulation environments; and the synchronization layer enables continuous bi-directional communication via a nine-tier IoT architecture inspired by FIWARE standards. A robot task assignment algorithm is introduced to support operational autonomy while maintaining human oversight. The system is designed to optimize horticultural workflows such as seeding and harvesting while allowing farmers to interact remotely through cloud-based interfaces. Compared to previous digital agriculture approaches, DigiHortiRobot enables closed-loop coordination among perception, simulation, and action, supporting real-time task adaptation in dynamic environments. Experimental validation in a hydroponic greenhouse confirmed robust performance in both seeding and harvesting operations, achieving over 90% accuracy in localizing target elements and successfully executing planned tasks. The platform thus provides a strong foundation for future research in predictive control, semantic environment modeling, and scalable deployment of autonomous systems for high-value crop production. Full article

Nitrogen (N) availability often limits primary productivity in terrestrial ecosystems, and arbuscular mycorrhizal fungi (AMF) can enhance plant N acquisition. This study investigated the interactive effects of N fertilization and AMF inoculation on N uptake, plant performance and phenolic acid content in Hyssopus officinalis L., with the aim of promoting sustainable N management in H. officinalis cultivation. A factorial randomized complete block design was employed to evaluate four AMF inoculation strategies (no inoculation, root inoculation, soil inoculation and combined root–soil inoculation) across three N application rates (0, 0.5 and 1,1 g N pot⁻¹ (7 L)) in a controlled greenhouse environment. Combined root and soil AMF inoculation alongside moderate N fertilization (0.5 mg N pot⁻¹) optimized N use efficiency, maximizing plant biomass and bioactive compound production. Compared to non-inoculated controls, this treatment combination increased N uptake by 30%, phosphorus uptake by 24% and potassium uptake by 22%. AMF colonization increased chlorophyll content and total phenolic compounds under moderate N supply. However, excessive N application (1 g N pot⁻¹) reduced AMF effectiveness and secondary metabolite accumulation. Notably, AMF inoculation without N fertilization yielded the highest levels of anthocyanin and salicylic acid, indicating differential N-dependent regulation of specific biosynthetic pathways. The interaction between AMF and N demonstrated that moderate N fertilization (0.5 g N pot⁻¹) combined with dual inoculation strategies can reduce total N input requirements by 50%, while maintaining optimal plant performance. These findings provide practical insights for developing N-efficient cultivation protocols in medicinal plant production systems, contributing to sustainable agricultural practices that minimize environmental N losses. Full article

Using recycled concrete (RC) created from building debris to capture, utilize, and sequester CO₂ is a green and sustainable development strategy. Before CO₂ curing, pretreatment can provide a suitable environment for the carbonation reaction of the RC, accelerate the carbonation rate of the RC, and enhance its performance. The effects of the pre-curing time and residual water–cement ratio (Re) on the carbon sequestration rate, carbon sequestration, carbonation depth, and mechanical strength of RC were investigated and validated through X-ray diffraction (XRD) and scanning electron microscopy (SEM). The study demonstrated optimal carbon sequestration properties at a pre-curing time of 5 days. The corresponding carbon sequestration rate, unit carbon sequestration, carbonation depth, and compressive strength were 23.17%, 19.88 g/kg, 15.79 mm, and 28.7 MPa, respectively. Optimal carbon sequestration performance occurred at a Re of 0.26. The measured values were 20.15% (carbon sequestration rate), 17.38 g/kg (unit carbon sequestration), 12.55 mm (carbonation depth), and 31.1 MPa (compressive strength). According to the XRD and SEM results, the effects of pre-curing time and Re were mainly seen in the conversion rate of CaCO₃ and a denser microstructure. This implies that improving the CO₂ curing effect by controlling the pre-curing time and Re can both alleviate the pressure of greenhouse gas emissions and increase the utilization efficiency of RC. Full article

Stope dilution is a major hidden cost driver for the underground operation, especially in terms of reducing ore quality, increasing the amount of processing feed, and effects on operational cost. Accurate calculation and consideration of planned and unplanned dilution and mining loss amounts are essential during mine planning. The user interface named D–Loss has been developed with MATLAB R2023b, which provides a multiparadigm numerical computing environment for faster and more practical calculation of these dilution amounts to address these challenges by quantifying dilution and linking them directly to economic and CO₂ emissions indicators. By determination and analysis of the stope overall dilution amounts, it helps us understand greenhouse gas emissions and ensures the efficient use of underground equipment. Calculation of stope dilution in a practical and rapid manner allows for stope design and operational improvements, which can help reduce dilution in underground operations. This progress is tracked through the D–Loss interface within the short- and long-term production planning. Moreover, by quantifying dilution impacts on comminution and haulage costs, D–Loss becomes a critical software for tracking economic losses and optimizing financial outcomes in the mining industry. D–Loss helps users iteratively assess the efficiency of updates and provides support in mine design, scheduling, and environmental impact control by comparing planning and operational improvements before and after. Full article

The use of chloride-based deicing salts, particularly sodium chloride (NaCl) and calcium chloride (CaCl₂), is a common practice in cold regions for maintaining road safety during winter. However, the accumulation of salt residues in adjacent soils poses serious environmental threats, including reduced pH, increased electrical conductivity (EC), disrupted soil structure, and plant growth inhibition. This study aimed to evaluate the combined effect of activated carbon (AC) and Pennisetum alopecuroides, a salt-tolerant perennial grass, in alleviating salinity stress under deicer-treated soils. A factorial greenhouse experiment was conducted using three fixed factors: (i) presence or absence of Pennisetum alopecuroides, (ii) deicer type (NaCl or CaCl₂), and (iii) activated carbon mixing ratio (0, 1, 2, 5, and 10%). Soil pH, EC, and ion concentrations (Na⁺, Cl⁻, Ca²⁺) were measured, along with six plant growth indicators. The results showed that increasing AC concentrations significantly increased pH and reduced EC and ion accumulation, with the 5% AC treatment being optimal in both deicer systems. Plant physiological responses were improved in AC-amended soils, especially under CaCl₂ treatment, indicating less ion toxicity and better root zone conditions. The interaction effects between AC, deicer type, and plant presence were statistically significant (p < 0.05), supporting a synergistic remediation mechanism involving both adsorption and biological uptake. Despite the limitations of short-term controlled conditions, this study offers a promising phytomanagement strategy using natural adsorbents and salt-tolerant plants for sustainable remediation of salt-affected soils in road-adjacent and urban environments. Full article

Organic amendments provide a sustainable strategy to enhance soil quality in degraded environments while also helping to reduce greenhouse gas emissions, for example, by improving soil structure, minimizing the use of synthetic fertilizers, and promoting a green economy. This study assessed the comparative effects of two organic amendments—vermicompost leachate and biochar—on the performance of popcorn maize (Zea mays L. var. everta) cultivated in saline soil conditions. Four treatments were evaluated: T0 (Control), T1 (Vermicompost leachate), T2 (Biochar), and T3 (Vermicompost leachate + Biochar), each with 10 replicates arranged in a Completely Randomized Design (CRD). Although various soil physicochemical, microbiological, and agronomic parameters displayed no significant differences compared to the control, the application of biochar resulted in considerable improvements in soil total organic carbon, the microbial community (mesophilic aerobic bacteria, molds, and yeasts), and increased seed length and diameter. In contrast, vermicompost leachate alone negatively impacted plant growth, leading to decreases in leaf area, stem thickness, and grain yield. Specifically, grain yield declined by 46% with leachate alone and by 31% when combined with biochar, compared to the control. These findings emphasize the superior effectiveness of biochar over vermicompost leachate as a soil amendment under saline conditions and highlight the potential risks of widely applying compost teas in stressed soils. It is recommended to conduct site-specific assessments and screenings for phytotoxins and phytopathogens prior to use. Additionally, the combined application of leachate and biochar may not be advisable given the tested soil characteristics. Full article

This study investigated how propagation systems, growth regulators, and hormone formulations interactively affect the rooting and subsequent growth of rosemary (Salvia rosmarinus Spenn) cuttings. A three factorial (3 × 2 × 7) experiment was conducted under a fully controlled greenhouse environment, incorporating three soilless propagation systems (mist, float, aeroponics), two rooting hormone formulations (powder and gel-based IBA), and two growth regulators (paclobutrazol and daminozide) at three concentrations each. Significant differences (p < 0.001) were found in shoot height, root length, and number of lateral roots. The float system combined with powder hormone and no retardants achieved the highest shoot height (mean = 16.7 cm), while aeroponics with powder hormone and daminozide 1000 ppm promoted the greatest root branching (mean = 12.2 lateral roots per cutting). Root length was maximized (mean = 15.9 cm) under float systems with daminozide 1000 ppm. High doses of both growth regulators negatively affected all parameters across systems. Post-transplantation monitoring confirmed that cuttings from float and mist systems treated with powder hormone and low or no growth retardants exhibited superior establishment and net growth over 60 days. These findings demonstrate the critical importance of pairing hormone type, regulator concentration, and propagation system, providing actionable protocols for nursery managers aiming to enhance Salvia rosmarinus propagation in commercial practice. Full article

Calcareous soils, characterized by high pH and calcium carbonate content, often limit the availability of essential nutrients for crops such as rice (Oryza sativa L.), reducing yield and nutritional quality. In this study, we evaluated the effect of the halotolerant yeast Debaryomyces hansenii on the growth, nutrient uptake, and phosphorus acquisition mechanisms of rice plants cultivated in calcareous soil under controlled greenhouse conditions. Plants inoculated with D. hansenii, particularly via root immersion, exhibited significantly higher SPAD chlorophyll index, plant height, and grain yield compared to controls. A modest increase (~4%) in dry matter content was also observed under sterilized soil conditions. Foliar concentrations of Fe, Zn, and Mn significantly increased in plants inoculated with D. hansenii via root immersion in non-sterilized calcareous soil, indicating improved micronutrient acquisition under these specific conditions. Although leaf phosphorus levels were not significantly increased, D. hansenii stimulated acid phosphatase activity, as visually observed through BCIP staining, and upregulated genes involved in phosphorus acquisition under both P-sufficient and P-deficient conditions. At the molecular level, D. hansenii upregulated the expression of acid phosphatase genes (OsPAP3, OsPAP9) and a phosphate transporter gene (OsPTH1;6), confirming its influence on P-related physiological responses. These findings demonstrate that D. hansenii functions as a plant growth-promoting yeast (PGPY) and may serve as a promising biofertilizer for improving rice productivity and nutrient efficiency in calcareous soils, contributing to sustainable agricultural practices in calcareous soils and other nutrient-limiting environments. Full article

Recent years have seen significant interest among agricultural researchers in using robotics and machine vision to enhance intelligent orchard harvesting efficiency. This study proposes an improved hybrid framework integrating YOLO VX deep learning, 3D object recognition, and SLAM-based navigation for harvesting ripe fruits in greenhouse environments, achieving servo control of robotic arms with flexible end-effectors. The method comprises three key components: First, a fruit sample database containing varying maturity levels and morphological features is established, interfaced with an optimized YOLO VX model for target fruit identification. Second, a 3D camera acquires the target fruit’s spatial position and orientation data in real time, and these data are stored in the collaborative robot’s microcontroller. Finally, employing binocular calibration and triangulation, the SLAM navigation module guides the robotic arm to the designated picking location via unobstructed target positioning. Comprehensive comparative experiments between the improved YOLO v12n model and earlier versions were conducted to validate its performance. The results demonstrate that the optimized model surpasses traditional recognition and harvesting methods, offering superior target fruit identification response (minimum 30.9ms) and significantly higher accuracy (91.14%). Full article

Greenhouse horticulture plays an important role globally by producing nutritious fruits and vegetables with high resource use efficiency. Modern greenhouses are large-scale high-tech production factories that are increasingly data-driven, and where climate and irrigation control are gradually becoming more autonomous. This is enabled by technological developments and driven by shortages in skilled labor and the demand for improved resource use efficiency. In the Autonomous Greenhouse Challenge, it has been shown that controlling greenhouse cultivation can be done efficiently with intelligent algorithms. For an optimal strategy, however, it is essential that control algorithms properly account for crop responses, which requires appropriate sensors, reliable data, and accurate models. This paper presents the results of the 4th Autonomous Greenhouse Challenge, in which international teams developed six intelligent algorithms that fully controlled a dwarf tomato cultivation, a crop that is well-suited for robotic harvesting, but for which little prior cultivation data exists. Nevertheless, the analysis of the experiment showed that all teams managed to obtain a profitable strategy, and the best algorithm resulted a production equivalent to 45 kg/m²/year, higher than in the commercial practice of high-wire cherry tomato growing. The predominant factor was found to be the much higher plant density that can be achieved in the applied growing system. More difficult challenges were found to be related to measuring crop status to determine the harvest moment. Finally, this experiment shows the potential for novel greenhouse cultivation systems that are inherently well-suited for autonomous control, and results in a unique and rich dataset to support future research. Full article

Global climate change has intensified the challenges of low-temperature, low-light, and high-humidity microclimates in North China’s greenhouses during winter, exposing the limitations of traditional controlled-environment agriculture (CEA) facilities. This study monitored air temperature, relative humidity, and light intensity in three greenhouse types—an externally insulated plastic greenhouse, soft-shell solar greenhouse, and brick-walled solar greenhouse—across three overwintering periods (pre-, mid-, post-) using high-precision sensors (monitoring period is from 1 October 2024 to 31 March 2025). A Comprehensive Evaluation Index (CEI) based on the entropy method was developed, integrating seven indicators (daily average temperature, temperature range, hours below 5 °C, average humidity, hours above 80% humidity, average light intensity, and light utilization efficiency) to systematically evaluate greenhouse microclimate regulation performance. Results showed that the brick-walled solar greenhouse exhibited superior thermal insulation, with nearly zero hours below 5 °C during mid-overwintering, while the soft-shell solar greenhouse achieved the highest light utilization efficiency (75.1–79.6%). The externally insulated plastic greenhouse exhibited the highest relative humidity (>80% for 13–19 h/day) but a poor thermal insulation performance. The CEI ranked the brick-walled solar greenhouse (0.86) and the soft-shell solar greenhouse (0.84) significantly higher than the externally insulated plastic greenhouse (0.39), with the relative humidity significantly negatively correlated with light indicators (P < 0.05), and the temperature and light indicators strongly correlated with the CEI (P < 0.01). Structural design and material innovation are critical for climate adaptation. Brick-walled and soft-shell solar greenhouses balance thermal and light performance, while the externally insulated plastic greenhouse faces structural limitations. The findings provide a scientific basis for greenhouse optimization and regional layout planning. Full article

This study investigates the impact of different combustion control strategies on marine engine combustion and emission characteristics at a high ammonia energy ratio. Compared to the strategy of maintaining a constant fuel injection duration, the strategy of keeping the fuel injection pressure constant allows the kinetic energy of diesel to remain at a higher level. This results in an increase in combustion efficiency and indicated the thermal efficiency of the engine, while also reducing CO₂ and soot emissions. However, when the ammonia energy ratio increases to more than 50%, the indicated thermal efficiency starts to decrease along with the increase in the emissions of N₂O and unburned ammonia. To address these issues, one of the potential means is to improve the in-cylinder combustion environment by increasing the in-cylinder gas temperature. This can enhance combustion efficiency and ultimately optimize the performance and emission characteristics of dual-fuel engines, which results in an increase in the combustion efficiency to 98% and indicated thermal efficiency to 54.47% at a relatively high ammonia energy ratio of 60%. Emission results indicate that N₂O emissions decrease from 1099 ppm to 25 ppm, while unburned ammonia emissions drop from 16016 ppm to 100 ppm. Eventually, the greenhouse gas emissions were reduced by about 85.3% in comparison with the baseline case. Full article

In the current context, where environmental concerns are gaining increased attention, the transition toward sustainable urban mobility stands out as a necessary and responsible step. Technological advancements over the past decade have brought private autonomous vehicles, particularly those defined by the Society of Automotive Engineers Levels 4 and 5, into focus as promising solutions for mitigating road congestion and reducing greenhouse gas emissions. However, the extent to which Autonomous Vehicles can fulfill this potential depends largely on user acceptance, patterns of use, and their integration within broader green energy and sustainability policies. The present paper aims to develop an integrated conceptual model that links behavioral determinants to environmental outcomes, assessing how individuals’ intention to adopt private autonomous vehicles can contribute to sustainable urban mobility. The model integrates five psychosocial determinants—perceived usefulness, trust in technology, social influence, environmental concern, and perceived behavioral control—with contextual variables such as energy source, infrastructure availability, and public policy. These components interact to predict users’ intention to adopt AVs and their perceived contribution to urban sustainability. Methodologically, the study builds on a narrative synthesis of the literature and proposes a framework applicable to empirical validation through structural equation modeling (SEM). The model draws on established frameworks such as Technology Acceptance Model (TAM), Theory of Planned Behavior, and Unified Theory of Acceptance and Use of Technology, incorporating constructs including perceived usefulness, trust in technology, social influence, environmental concern, and perceived behavioral control, constructs later to be examined in relation to key contextual variables, including the energy source powering Autonomous Vehicles—such as electricity from mixed or renewable grids, hydrogen, or hybrid systems—and the broader policy environment (regulatory frameworks, infrastructure investment, fiscal incentives, and alignment with climate and mobility strategies and others). The research provides relevant directions for public policy and behavioral interventions in support of the development of clean and smart urban transport in the age of automation. Full article