Search Results (461)

Optimizing planting density is a critical, cost-effective strategy for sustainable agricultural intensification, yet moving beyond static recommendations to environment-specific precision management remains a key challenge. This study establishes a three-step framework (comprising zoning, response extraction, and machine learning modeling) to determine optimum planting density (OPD) for rice (Oryza sativa L.). Utilizing a data-driven synthesis of 960 field observations from the Northeast Black Soil Region (NBSR) of China, we identified distinct spatial variability in OPD (16.6 to 37.4 × 10⁴ hills ha⁻¹). Northern regions computationally prioritized higher densities, aligning with agronomic strategies to offset thermal constraints, while southern regions favored lower densities to reduce canopy competition. Soil properties, particularly Soil Organic Carbon (SOC), pH, Cation Exchange Capacity (CEC), and Total Nitrogen (TN), were identified as the dominant predictive indicators, collectively surpassing climatic factors in their predictive importance. This highlights the foundational role of soil buffering capacity in estimating crop tolerance to density management. Based on model-derived estimates, optimized density management indicated potential yield improvements of 3.8% to 9.7% (up to 872.32 kg ha⁻¹) compared to conventional practices. By replacing uniform practices with dynamic, environment-driven strategies, this work contributes to Sustainable Development Goals (SDGs) 2 (Zero Hunger), 12 (Responsible Consumption and Production), and 13 (Climate Action), offering a scalable solution for diverse rice production systems under climate change. Full article

Electric Vehicles (EVs) can contribute significantly to reducing greenhouse gas emissions and addressing climate change problems. Modern EVs are primarily powered by electrochemical batteries such as lead-acid (Pb-acid), nickel-metal hydride (NiMH), sodium-ion (Na-ion), solid-state and lithium-ion (Li-ion) batteries. When compared to other battery types, Li-ion batteries are the most suitable for EV applications due to their practical features such as their high energy density, high charging and discharging efficiency and extended lifetime. However, the main risk of Li-ion batteries is that they are exposed to thermal runaway phenomena, which raises severe concerns about the safety of EV propulsion systems. Thermal runaways should be considered carefully as they cannot be stopped once they start and can lead to battery explosion. One of the main reasons leading to this phenomenon is abusing the state of charge (SoC) of the battery. Therefore, the battery management system (BMS) plays a crucial role in mitigating the stimulation of the thermal runaway process by accurately estimating and properly managing the battery cells. To help researchers and designers with understanding this matter, this paper proposes a review of the most effective SoC estimation methods for EV Li-ion batteries and links these methods with practical energy management systems in the EV market. Full article

High-resolution mapping of soil organic carbon (SOC) in arid regions remains challenging. Using Xinjiang as a case study, this research constructed a prediction framework integrating Boruta feature selection with the Random Forest (RF) algorithm to achieve refined mapping of topsoil SOC. Results indicated that: (1) Among the tested machine learning models, the Boruta–RF framework achieved the highest predictive performance (R² = 0.48, with the lowest RMSE); (2) Evapotranspiration (ET) and Vapor Pressure Deficit (VPD) were dominant drivers, with the stepwise increase in ET and negative inhibition of VPD confirming the decisive role of hydrothermal fluxes in regulating carbon input; (3) The total SOC storage was estimated at approximately 3.20 Pg C. Despite low carbon density, the desert ecosystem contributed 44.33% of the total storage, constituting a massive Sparse Carbon Pool. This study confirms the necessity of incorporating hydrothermal parameters and highlights that neglecting desert ecosystems leads to a significant underestimation of regional carbon storage. Full article

This work presents the development and validation of a 1D finite-volume model for the simulation of planar solid oxide cells (SOCs), developed for integration in more complex systems and process simulations. The model allows to investigate the temperature, composition, and current density profiles along the channel. In this work, the Fick’s equations typically used to calculate the concentration overpotential due to H₂ and H₂O diffusion in the electrode are improved compared to 1D SOC models available in the literature. In particular, the approximate analytical solution of the dusty gas model (DGM) equations allows for a better definition of H₂ and H₂O mixture diffusion coefficients, which are relevant, for instance, in the case of solid oxide fuel cells (SOFCs) fed with reformate gas mixtures. Differently from other 1D models available in the literature, the model developed is validated using experimental SOFC polarization curves covering a wide range of operating conditions in terms of molar fraction of H₂ (21–93%) and H₂O (7–50%) in the fuel, temperature (550–750 °C), and fuel utilization factor (exceeding 90%), demonstrating that 1D SOC models retain a good description of the physical processes occurring within the cell. While this work focuses on a co-flow SOFC configuration, the model can simulate a counter-flow configuration and electrolysis operation without modifying the model equations. Full article

This study evaluated the influence of soil health in riparian and ecotone zones on water quality in four high-Andean rivers (Atillo, Ozogoche, Yasepan, and Cebadas) within the Cebadas River sub-basin, Ecuador. Soil and water samples were collected from 20 sites during three field campaigns (2022–2024). Soil properties included organic carbon concentration, soil organic carbon stock (SOC), bulk density, moisture, and potential microbial activity estimated through laboratory CO₂–C efflux. Water quality parameters were integrated into the National Sanitation Foundation Water Quality Index (NSF-WQI), and riparian condition was assessed using the QBR-And index. Multivariate statistical approaches, including Random Forest and Classification and Regression Trees (CART), were used to identify the most influential predictors of ecosystem quality. Results revealed marked spatial contrasts. Riparian SOC stocks ranged from 22.8 to 32.8 Mg C/ha in the more disturbed Cebadas and Yasepan rivers to 91.4–133.6 Mg C/ha in the better-conserved Atillo and Ozogoche systems. Sites with higher SOC and lower bulk density consistently exhibited better water quality, with NSF-WQI values classified as “good”, whereas more degraded sites showed lower riparian quality and “fair” water quality. Riparian forest quality was strongly correlated with water quality (r = 0.81). Random Forest models identified ammoniacal nitrogen, fecal coliforms, and altitude as the most influential predictors of riparian ecosystem condition. These findings demonstrate that soil health and riparian integrity are tightly linked to water quality patterns in high-Andean fluvial systems and support their integration into ecosystem-based watershed management. Full article

Accurate state estimation is a key technology for improving battery utilization and ensuring operational safety in electric vehicles. The joint estimation of the state of charge (SOC) and the state of power (SOP) over a wide temperature range is therefore essential for intelligent battery management systems. To address modeling uncertainties and estimation accuracy degradation induced by ambient temperature variations, a dual-polarization equivalent circuit thermal model incorporating temperature bias is proposed, and online parameter updating is achieved using the forgetting factor recursive least squares (FFRLS) algorithm. Furthermore, an unscented particle filter (UPF) is constructed by employing the unscented Kalman filter (UKF) as the proposal density function of the particle filter, thereby improving the estimation accuracy and convergence speed of SOC under wide temperature conditions. Based on the coupling relationship between SOC and SOP, a stepwise progressive strategy is then developed to predict the peak power state under multiple constraints, enhancing the robustness of SOP estimation. Simulation and experimental results demonstrate that the proposed method can accurately estimate SOC and SOP under complex operating conditions over a wide temperature range from −5 °C to 45 °C, exhibiting favorable convergence performance and estimation accuracy, which contributes to the safe operation and performance optimization of electric vehicle battery systems. Full article

Because of their great energy density and efficiency, lithium-ion batteries (LIBs) are essential to renewable energy systems and electric vehicles. Effective battery management requires precise estimation of the state of health (SoH) and state of charge (SoC). In order to overcome the difficulties caused by parameter fluctuations and real-world disturbances, this work presents a novel μ-analysis-based methodology designed to improve the resilience and accuracy of online SoC and SoH estimations in LIBs. In contrast to conventional techniques, the suggested strategy successfully manages both structured and unstructured uncertainties in battery systems by combining μ-analysis with model-based estimation. The framework creates an estimator that is resistant to parameter drift and outside perturbations by combining model-based estimation approaches with μ-analysis tools. Simulations using UDDS, US06, and HWFET driving cycles are used to verify its performance. When evaluating battery health and condition in dynamic and uncertain operating scenarios, the μ-analysis-based estimator demonstrates superior accuracy compared to conventional H∞-pole placement filter methods. The proposed approach enhances system robustness, achieving an 8 dB improvement in disturbance attenuation, as verified through MATLAB/Simulink. Stability analysis reveals the μ-analysis controller maintains robust performance up to ‖∆‖∞ = 3.5 at 10 Hz, compared to only ‖∆‖∞ = 1.5 for the H∞-pole placement controller—demonstrating significantly greater tolerance to parameter variations and unmodeled dynamics. These capabilities make the μ-analysis approach particularly suitable for electric vehicle applications requiring next-generation battery management systems. Full article

To investigate the effects of long-term continuous rotary tillage incorporation (RT) on Fraxinus chinensis Roxb. plantations, this study compared 7- and 15-year-old stands subjected to RT since afforestation with their non-tilled counterparts (CK). Results demonstrated that RT significantly enhanced tree growth by synergistically improving soil nutrient availability, physical properties, and microbial community structure and function: (1) Compared with CK, RT increased diameter at breast height (DBH) by 28.89% in 7-year-old stands and 22.58% in 15-year-old stands, and tree height by 19.51% in 7-year-old stands and 25.00% in 15-year-old stands; (2) RT increased contents of soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP), rearranged the distribution patterns of soil particulate organic carbon (POC) and mineral-associated organic carbon (MAOC), and reduced soil bulk density (BD) and soil water content (SWC); (3) RT regulated microbial diversity, co-occurrence networks, and carbohydrate-degrading gene abundances, with more prominent effects in 15-year-old stands. This tillage practice is feasible and effective, and thus is recommended for application in F. chinensis plantation management, providing a scientific basis for refined and sustainable plantation management. Full article

There are 266 nature parks in Türkiye, including Aşıkpaşa Nature Park, covering a total area of approximately 109,023 ha; however, information regarding soil organic carbon stocks (SOCS), soil nitrogen stocks (NS), and nutrient stoichiometry in these protected forests remains limited. This study evaluates the influence of tree species, altitude, aspect, and soil depth on nutrient stocks and stoichiometry using a 3 × 2 × 3 × 3 factorial experimental design. The findings indicate that mixed stands (Black Pine + Cedar) significantly optimize nutrient storage, reaching peak N (3.531 ± 0.115 t ha⁻¹) and P (0.948 ± 0.016 t ha⁻¹) stocks. SOC and N stocks reached 66.34 ± 1.86 t ha⁻¹ and 4.032 ± 0.123 t ha⁻¹, respectively, along the altitudinal gradient. Soil pH exhibited a steady rise with altitude (from 7.86 to 8.15), contrary to typical leaching patterns, while bulk density varied depending on Altitude × Aspect × Depth interactions. Stoichiometric analyses revealed that Cedar stands maintain higher C:K ratios (3.457 ± 0.258), reflecting superior nutrient use efficiency. Furthermore, sunny aspects prioritized nitrogen mineralization (N:P ratio: 4.540), whereas shaded aspects facilitated phosphorus retention. These results prove that soil fertility and carbon sequestration are modulated by complex topographic–biotic interactions, suggesting that preserving mixed forest structures is of vital importance for ecological sustainability and forest resilience. Full article

Organic amendments, including biochar and compost, are widely recognized for their potential to improve soil health, but their linkage to soil water functions (e.g., storage, infiltration, plant availability) is not clear. Over two years (2024–2025), we investigated soil water infiltration and associated soil health properties in response to soil amendment application under no-tillage conditions in semi-arid agroecosystems of the southwestern USA. Soil water infiltration was measured in biochar, compost, biochar and compost, and control plots using the SATURO dual-head infiltrometer. Soil physical and chemical properties, including bulk density (BD), soil moisture content (SMC), water-filled pore space (WFPS), residue cover, mean weight diameter (MWD) of dry aggregates, water-stable aggregates (WSA), pH, soil organic carbon (SOC), and total nitrogen (TN), were assessed at 0–15 cm soil depth. The results show a 31.5% higher infiltration rate along with, a small but statistically significant (3.7% lower) bulk density, and 119% greater wet aggregate stability in the biochar-amended plots than in the control plots. Compost with biochar also improved soil health, but infiltration responses were variable. Infiltration was positively correlated with residue cover and soil pH, whereas it was negatively correlated or not correlated with other soil properties. This study demonstrates that biochar under no-tillage conditions can enhance soil health and resilience of semi-arid agroecosystems by improving soil water functions. Full article

Automotive System-on-Chips (SoCs) must meet stringent functional safety standards, such as ISO 26262 and IEC 61508, to ensure reliable operation under hardware faults. FPGA-based fault injection has emerged as a practical and cost-effective technique for functional safety verification. However, instrumentation-based methods face scalability challenges when applied to the high fault densities typical of automotive SoCs. To address these challenges, we propose a hybrid cascaded fault-injection controller architecture (HCCA-SAFE) that simultaneously reduces high-fanout global nets and eliminates long serial propagation paths. The architecture constrains enable-signal cluster width and distributes control across cascaded stages, improving timing results and routability under limited FPGA resources. The proposed architecture is evaluated on multiple open-source RISC-V processor cores. On openE902, HCCA-SAFE reduces net delay from 27.276 ns to 22.535 ns and achieves 32.2% and 63.8% lower net delay compared with the representative centralized and shift-chain approaches, respectively. On openE906, the proposed HCCA-SAFE limits the net delay to 12.959 ns and reduces the maximum control-signal fanout to 1763, respectively, compared with 25.825 ns and 40.442 ns in the conventional method. On openC906, the proposed design lowers the maximum control-signal fanout from 7725 to 570 and reduces the net delay to 7.506 ns. Furthermore, HCCA-SAFE produces results fully consistent with software-based RTL simulation, while delivering substantial performance gains. Speed-up factors of $127\times$, $206\times$, and $2123\times$ are achieved on openE902, openE906, and openC906, respectively, with efficiency improvements scaling with processor complexity These results confirm that HCCA-SAFE delivers scalable, timing-robust fault-injection control suitable for large automotive SoCs. Full article

The conversion of native forests into agricultural areas without conservation practices can expose tons of soil organic carbon (SOC) to the atmosphere. This study aimed to evaluate the effect of land use and cover (LULC) on C in regions of the Caiabi (SBC) and Renato (SBR) River sub-basins, located in the Brazilian Cerrado–Amazon transition. Data on physical attributes and SOC were obtained by region (upper, middle, and lower), LULC (cropland, pasture, and native forest), and depth (0–10, 10–20, and 20–40 cm), with five replicates for each variable. The highest SOC values were found in areas with higher clay contents or in areas of native forest or crop residues. In the SBC, there was a negative correlation of SOC with sand and particle density (PD) and a positive correlation with silt. In the SBR, there was a positive correlation between SOC and microporosity and total porosity, and a negative correlation with sand, soil bulk density, and PD. The highest SOC values were found in the SBC upper region, in native forest (107 Mg ha⁻¹), cropland (69 Mg ha⁻¹), and pasture (49 Mg ha⁻¹). In the SBR upper region, the values were highest in pasture and cropland (93 and 58 Mg ha⁻¹), and in the lower region, the values were highest in native forest (48 Mg ha⁻¹). SOC varied in relation to the SBC and SBR regions, the LULC, depth, and physical attributes, especially soil texture. Full article

Tillage practices critically influence soil’s physical properties, which are fundamental to sustainable agriculture in temperate climates. This review evaluates how conventional tillage (CvT; e.g., moldboard and chisel plowing), reduced tillage (RT), and conservation tillage (CT), particularly no-tillage (NT), affect six key indicators: bulk density (BD), saturated hydraulic conductivity (Ks), wet aggregate stability (WAS), penetration resistance (PR), available water capacity (AWC), and soil organic carbon (SOC). Special emphasis is placed on differentiating topsoil and subsoil responses to inform climate-resilient land management. A total of 70 peer-reviewed studies published between 1991 and 2025 were analyzed. Data were extracted for BD, Ks, WAS, PR, AWC, and SOC across tillage systems. Depths were standardized into topsoil (0–10 cm) and composite (>10 cm) categories. Descriptive statistics were used to synthesize cross-study trends. NT showed lower mean BD in the topsoil (1.32 ± 0.08 Mg/m³) compared with moldboard plow (1.33 ± 0.09) and chisel tillage (1.39 ± 0.12); however, the effects of tillage on BD were not statistically significant, while BD was higher at composite depths under NT (1.56 ± 0.09 Mg/m³), indicating subsoil compaction. Ks improved under NT, reaching 4.2 mm/h with residue retention. WAS rose by 33.4%, and SOC increased by 25% under CT systems. PR tended to be elevated in deeper layers under NT. Overall, CT, particularly NT, improves surface soil’s physical health and SOC accumulation in temperate agroecosystems; however, persistent subsoil compaction highlights the need for depth-targeted management strategies, such as controlled traffic, periodic subsoil alleviation, or deep-rooted cover crops, to sustain long-term soil functionality and climate-resilient production systems. Full article

Field capacity (FC) and permanent wilting point (PWP) thresholds are critical parameters in climate-smart agriculture because they directly relate to soil water availability, which is essential for optimizing water use, improving crop yields, and ensuring resilience against climate variability. Using the continuous mosaic of SoilGrids data, pedotransfer functions based on bulk density, clay content, and sand content were applied to estimate the threshold values of FC and PWP across Mexico utilizing random forest (RF) algorithms. The selection of these parameters was based on their positive contribution to the model’s prediction: bulk density (0.51), clay content (0.21), and sand content (0.16). Soil organic carbon (SOC) contributed negatively; this negative importance score warrants careful interpretation. The 30–60 cm depth was chosen based on the assumption that it is reasonably uniform across other depths and lies below the highly variable surface horizon, which is strongly influenced by management practices and organic matter dynamics. Here we address key technical and scientific critiques regarding the use of SoilGrids for generating FC and PWP data. Additionally, the relevant role of FC and PWP thresholds in the context of climate-smart agriculture is highlighted, from the calculation of available soil water to their role in achieving sustainable development goals. Full article

To address the challenges of low land use efficiency, soil degradation, and high management costs in Ilex asprella cultivation, this study established an I. asprella–Grona styracifolia intercropping system and systematically evaluated its effects on soil nutrient cycling, microbial communities, and crop growth. Field experiments were conducted in Yunfu City, Guangdong Province, with monoculture (LCK for I. asprella, DCK for G. styracifolia) and three intercropping densities (HDT, LDT, MDT). Combining 16S rRNA sequencing and metagenomics, we analyzed the functional profile of the rhizosphere microbiome. The results showed that intercropping significantly increased the biomass of G. styracifolia, with the medium-density (MDT) treatment increasing plant length and fresh weight by 41.2% and 2.4 times, respectively, compared to monoculture. However, high-density intercropping suppressed the accumulation of medicinal compounds. In terms of soil properties, intercropping significantly enhanced soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), and available nitrogen (AN) in the rhizosphere of both plants. Specifically, AN in the I. asprella rhizosphere increased by 18.9%. Soil urease and acid phosphatase activities were also elevated, while pH decreased. Microbial analysis revealed that intercropping reshaped the rhizosphere microbial community structure, significantly increased the Shannon diversity index of bacteria in the G. styracifolia rhizosphere, and enhanced the complexity of the microbial co-occurrence network. Metagenomic analysis further confirmed that intercropping enriched functional genes related to carbon fixation, nitrogen cycling (nitrogen fixation, assimilatory nitrate reduction), and organic phosphorus mineralization (the phoD gene), thereby driving the transformation and availability of soil nutrients. These findings demonstrate that the I. asprella–G. styracifolia intercropping system, particularly at medium density, effectively improves soil fertility and land use efficiency by regulating rhizosphere microbial functions, providing a theoretical basis for the sustainable ecological cultivation of I. asprella. Full article