Search Results (4,366)

Urban forest parks (UFPs) are important recreational green spaces within the urban built environment, contributing to urban residents’ subjective vitality and well-being. Understanding how the motivations of visitors to urban forest parks translate into positive psychological experiences is crucial for promoting urban residents’ physical and mental health. This study concentrated on the motives for visiting UFPs in Fuzhou, China, and developed a conceptual framework of “motivation–attitude–visit frequency–experience” to explore the pathways through which two dimensions of motivation influence leisure experience. Data were obtained from the responses of 573 visitors (242 males, 331 females) by using on-site intercept sampling, and online and on-site questionnaire survey at seven UFPs across the city. Serial mediation analysis was used to test the proposed relationships. The results showed that both extrinsic and intrinsic motivation positively predicted visit attitudes (β = 0.297, p < 0.001; β = 0.309, p < 0.001); only intrinsic motivation had a significant positive impact on visit frequency (β = 0.147, p = 0.031), and both visit attitudes and visit frequency positively predicted leisure experiences (β = 0.371, p < 0.001; β = 0.402, p < 0.001). Extrinsic motivation was entirely mediated by attitude, forming an indirect path to leisure experiences, whereas intrinsic motivation influenced experiences through both direct paths and indirect paths. The findings reveal the important role of attitude in linking visitors’ motivations with urban forest park use and experiential outcomes. Decision-makers should consider visitors’ motivational orientations and psychological responses in the planning, design, and management of urban forest parks to promote more meaningful and sustainable engagement with urban nature. With complexities of cities, more attention needs to be given to the use of longitudinal studies in future research to obtain comparable findings and further generalizations. Full article

With the acceleration of global urbanization, neighborhood green spaces have emerged as important carbon sinks. However, current urban carbon inventories frequently neglect the understory shrub layer owing to morphological heterogeneity and a lack of standardized allometric models. To address this limitation, we analyzed 13 major shrub species (n = 665) through whole-plant excavation. Hierarchical cluster analysis and linear discriminant analysis classified the 13 species into three functional morphological groups based on intrinsic morphological traits (basal stem density, root-to-shoot biomass allocation, and secondary radial growth capacity) (p < 0.001): small shrubs (Type I), large shrubs with high root-to-shoot allocation (Type II), and multi-stemmed sprouting shrubs (Type III). Standard models accurately estimated biomass for Type I species, whereas symbolic regression improved the prediction of the complex non-linear biomass allocation of Type II species. For Type III species, characterized by multi-stemmed growth and anthropogenic management, robust regression provided stable biomass estimates. Gompertz growth models predicted carbon sequestration trajectories, indicating that urban shrubs function as rapid carbon sinks during the early establishment stage. To facilitate practical application, we developed the Urban Forest Carbon Storage Calculator, which integrates Monte Carlo simulation and bootstrapping to generate 95% confidence intervals for species-specific biomass estimation. This study quantifies the overlooked carbon value of the urban shrub layer and provides a morphology-driven methodological approach and a practical tool for sustainable urban forest management. Full article

Accurate prediction of plough layer thickness (PLT) in cropland is essential for soil quality assessment and sustainable land management, yet regional-scale PLT mapping remains challenging because PLT is a subsurface structural attribute that cannot be directly retrieved from surface spectral signals. This study developed an interpretable framework for province-scale mapping of cropland plough layer thickness (PLT) in Hubei Province, China, by integrating multi-source remote sensing observations, including Landsat 8 optical spectral bands, Sentinel-1 SAR backscatter, and crop dynamic spectral response metrics derived from multi-year Enhanced Vegetation Index (EVI) time series, together with topographic, climatic, soil physicochemical, and land-use variables. A total of 1926 cropland soil samples were used to train and validate random forest (RF), extreme gradient boosting (XGBoost), and Cubist models, while prediction uncertainty was quantified using 90% prediction intervals. The relative contributions of different environmental variable groups were assessed, and Shapley Additive Explanations (SHAP) were used to interpret key predictors. The all-variable scenario achieved the best overall performance, with RF showing the highest accuracy (R² = 0.46; RMSE = 3.12 cm) and the narrowest prediction interval. Climatic and topographic factors dominated PLT spatial variability, whereas other variable groups provided complementary predictive information. These findings demonstrate the potential of integrating multi-source environmental data and interpretable machine learning for regional PLT mapping, and the mapped distribution of cropland PLT provides a spatial basis for cropland quality assessment and targeted soil management, although further improvements will require spatially explicit agricultural management information and more direct PLT-related predictors. Full article

Accurate short-term travel time prediction is foundational for sustainable urban mobility and intelligent transportation systems on urban arterial corridors, where travel conditions are shaped by interacting traffic, weather, and public transport factors. This study proposes a multi-source machine learning framework for segment-direction-level prediction in the Denizli city center. Floating car data (FCD), Traffic Control Center (TCC) inductive loop detector measurements, historical weather, and public transport indicators were integrated into a 15 min time-segment structure. The final dataset includes 60 segment-direction targets. Performance was evaluated using Linear Regression, Random Forest, LightGBM, and LSTM under a chronological train-validation-test design. Tree-based ensemble models produced the most stable overall performance, with LightGBM and Random Forest yielding similarly low pooled test errors. Segment-level analyses revealed clear spatial and temporal heterogeneity, showing no single model is universally superior across all links. By providing reliable traffic-state information, the framework enables efficient traffic management and may indirectly reduce delay, fuel use, and emissions; these environmental effects were not quantified. SHAP-based interpretation showed that temporal and traffic-state variables dominate predictions, while weather and public transport provide complementary value. Full article

Soil compaction alters soil structure and hydrology by increasing bulk density and penetration resistance, disrupting aggregates, reducing porosity and infiltration capacity, and enhancing shear strength. These changes can impede seedling establishment, particularly by restricting root development. The objective of this study was to evaluate the effects of a controlled soil penetration resistance (SPR) gradient on velvet maple (Acer velutinum Boiss.) seedling growth, morphology, and architecture under nursery conditions in northern Iran’s Hyrcanian forests. We hypothesized that increasing compaction would reduce seedling size and total biomass while altering the pattern of biomass partitioning between above- and belowground organs. Seven compaction levels were induced using a 4.736 kg hammer dropped from 45.7 cm, applying 0 (control), 1, 2, 4, 6, 8, or 10 blows per layer across 35 pots (5 replicates per level). After the 227-day growing period, seedlings were carefully excavated; root systems were gently washed, and all morphological indicators (main root length, lateral root length, and stem length) were measured manually using a ruler. Soil compaction resulted in a marked increase in soil penetration resistance (SPR), rising from 0.32 ± 0.03 MPa in the control treatment to 2.09 ± 0.08 MPa at the highest compaction level (p ≤ 0.01). Elevated SPR elicited significant polynomial responses in seedling morphological traits, including stem and root dimensions, as well as in biomass accumulation (total, shoot, and root). Notably, the main root biomass exhibited a significant decline with increasing compaction intensity, whereas lateral root length increased correspondingly, despite a reduction in lateral root biomass. These findings indicate that, in loam to clay-loam soils under optimal moisture conditions, total seedling biomass increased with moderate compaction, peaking near 1.5 MPa, whereas significant root growth reduction occurred above approximately 1.2 MPa. Above this level, compensatory lateral root elongation and a shift in biomass partitioning toward shoots were observed, but severe compaction (>1.5 MPa) ultimately suppressed whole-plant productivity. The study identifies a novel root architectural trade-off characterized by a 198% increase in specific root length and a tripling of the lateral-to-main root length ratio under severe compaction, providing species-specific thresholds and early-warning indicators for nursery soil management. These findings provide valuable insights into adaptive seedling responses to soil compaction and contribute to the development of sustainable forest nursery practices. Improving seedling quality under compacted soil conditions can enhance plantation success, soil conservation, and the long-term sustainability of forest restoration programs. Full article

Norway spruce (Picea abies L.) growing in mountain forests is a sensitive indicator of environmental change, but its growth response may be strongly modified by local site conditions, which is important for sustainable forest management under climate change. This study examined two spruce populations in the upper montane zone of the Śnieżnik Massif, Eastern Sudetes, southwestern Poland: one growing on a raised bog (Sadzonki—TS) and the other on mineral soil (Owczarnia—OW). Tree-ring width (TRW) chronologies were developed from 59 sampled trees represented by increment cores and stem discs, covering 1781–2023 for TS and 1800–2023 for OW. Annual δ¹³C analysis was performed for a selected TS tree, and decadal wood geochemistry was analyzed for a selected disc. Spruces growing on the raised bog showed tree-ring widths that were less than half those of trees growing on mineral soil. TRW was mainly controlled by summer or spring–summer temperature, while precipitation had a stronger positive effect at the mineral-soil site. δ¹³C showed weak relationships with TRW but negative correlations with mean annual and summer temperature. Elevated concentrations of Na, K, Ca, Mg, Fe, Mn, Cu, Zn, and Pb occurred mainly in 1941–1950 and 1971–1980, indicating the influence of historical air pollution. The contrasting growth trends and health status of the two populations demonstrate that local habitat conditions strongly shape spruce responses to environmental change and should be considered in sustainable mountain forest conservation and adaptation strategies. Full article

Accurate identification of eucalyptus plantation maturity, which is hierarchically defined as young forests, middle-aged forests, and mature forests, corresponding to different growth and physiological development stages of eucalyptus stands, is critical for forestry management and sustainable development. Currently, research on fine-grained semantic segmentation of different eucalyptus growth stages remains insufficient, and there is a lack of systematic evaluation of classical deep learning architectures and multimodal data for eucalyptus plantation maturity identification. This limits the application of remote sensing technology in forestry management, and constrains the understanding of growth dynamics in subtropical planted forests. Taking Gaofeng Forest Farm in Nanning, Guangxi, as the study area, this study analyzes the adaptability of four generations of deep learning segmentation architectures—U-Net (convolutional baseline), Trans-UNet (CNN-Transformer hybrid), Swin-UNet (pure Transformer), and Mamba-UNet (state-space model)—in the fine identification of eucalyptus plantation maturity based on spectral indices (SIs), C-band SAR data (S1), and multispectral data (S2). The results show the following: (1) There is no positive correlation between model complexity and recognition performance. Among all architectures, Mamba-UNet achieves the best performance, with a validation set mIoU of 79.10% and an F1-score of 88.26%. The performance ranking of the different architectures evaluated is Mamba-UNet > Swin-UNet > U-Net > Trans-UNet. (2) The S2+SI combination achieves the highest accuracy (mIoU 79.10%), outperforming S2+S1+SI (78.55%), S2+S1 (78.38%), and single S2 (77.72%), which indicates the strong correlation between spectral indices and eucalyptus physiological characteristics. The backscattering features of S1 are limited by canopy penetration in the subtropical rainforest, introducing redundancy and triggering negative fusion effects. (3) Independent verification with field survey points verifies the strong generalization ability of the proposed approach, with OA of 94.08%, mIoU of 85.62%, Precision of 93.00%, Recall of 91.37% and F1-score of 92.15%, which indicates that the methodological framework can realize the identification of eucalyptus maturity with high precision. (4) Eucalyptus accounts for 60.15% of the total area of Gaofeng Forest Farm. Within the eucalyptus stand age structure, young, middle-aged, and mature forests account for 20.67%, 13.62%, and 25.86%, respectively. The overall distribution exhibits a polarized pattern with high proportions of young and mature forests. The findings offer theoretical support and insights for dynamic monitoring of fast-growing plantations and refined management of stand development stages. Full article

The Internet of Plants (IoP) represents the convergence of Artificial Intelligence (AI), Big Data analytics, and the Internet of Things (IoT) within protected agricultural systems. This study presents an IoP platform designed to collect, process, and analyze real-time environmental data using specialized IoT sensors capable of monitoring critical variables, including carbon dioxide concentration (CO₂), pH, air temperature, and relative humidity in hydroponic production systems. The proposed framework integrates advanced machine-learning algorithms, including Random Forest Regressor and Long Short-Term Memory (LSTM) neural networks, to process large volumes of environmental data and support crop management. In addition, the platform incorporates Vapor Pressure Deficit (VPD) and Growing Degree Days (GDD) analyses to provide crop-specific recommendations and support informed decision-making. This platform establishes a benchmark for smart agriculture in Mexico’s Laja–Bajío region, facilitating informed decision-making and maximizing the sustainability of food systems. Experimental validation was conducted under both controlled and semi-controlled environments using Swiss chard (Beta vulgaris subsp. cicla L.) and lettuce (Lactuca sativa L.) cultivated in hydroponic systems. These environments represented contrasting climatic conditions, allowing evaluation of platform stability and forecasting performance under varying thermal regimes. The Random Forest Regressor model, trained using growth chamber data consisting of 19,836 valid observations, reproduced the deterministic VPD relationship with a coefficient of determination (R²) of 0.90 and a root mean square error (RMSE) of 0.08 kPa, confirming internal consistency and identifying temperature as the dominant contributing variable rather than predicting an independent outcome. The dynamic alarm system, integrated with crop phenological stages, demonstrated greater effectiveness than conventional static-threshold approaches by generating alerts according to crop developmental requirements. Furthermore, the web-based visualization platform enabled users to interpret environmental conditions through intuitive graphical representations, facilitating decision-making without requiring specialized technical expertise. The results demonstrate the feasibility of the IoP platform as a comprehensive environmental management tool for protected agricultural systems. The proposed framework provides a scalable solution for precision agriculture applications in the Laja–Bajío region of Mexico and in other regions with similar production systems. Full article

Nature reserves are among the regions with the richest forest and grass resources. They face dual pressures of protection and development. The non-timber forest products (NTFPs) industry combines ecological protection with economic development. This unique function provides a key solution to this contradiction. Drawing on the Theory of Planned Behavior (TPB), this study employs a structural equation model (SEM). We collected survey data from 361 farmers. These farmers live in communities surrounding Yunling Provincial Nature Reserve. We empirically examine how behavioral attitude, subjective norm, and perceived behavioral control influence their willingness to participate in the non-timber forest products (NTFPs) industry. The results show that subjective norm and perceived behavioral control significantly enhance participation willingness. Subjective norm emerges as the strongest predictor. In contrast, behavioral attitude has no significant effect. This suggests that external social pressure and perceived self-capability outweigh simple benefit expectations in shaping willingness. Accordingly, we recommend three measures. First, strengthen external support to translate attitudes into actual willingness. Second, leverage social networks to amplify subjective norms. Third, enhance farmers’ endogenous capacity to consolidate their participation base. These measures can foster a win–win outcome for ecological protection and community income growth. Full article

The expansion of sustainable timber harvesting on peatland forests is important for forest owners, contractors, and the forest industry. This study investigated the effects of site-specific conditions on summertime harvesting operations and modeled the influence of a light nine-ton forwarder on rut formation. Three logistic mixed-effects models predicted peat surface disturbance (ROC = 0.60, 0.63, and 0.67), while a linear mixed model predicted rut depth when rutting occurred (R² = 0.35). Rut formation was associated with the number of machine passes, cumulative load, groundwater table depth, peat layer thickness, and interactions between stand and harvesting variables. The results indicate that timber can be successfully forwarded from drained peatlands with limited bearing capacity when operations are adapted to local site conditions. The findings highlight the importance of road-network planning, load management, and operator decision-making in reducing rut formation. This modeling approach also supports operator training, thereby contributing to more sustainable timber harvesting on low-bearing-capacity sites. Full article

Dispersal mechanisms of phytopathogenic fungi and oomycetes are critical components of forest disease dynamics. While wind and water are well-studied pathways, the role of large forest herbivores as passive vectors remains significantly overlooked. This study quantifies and compares the pathogen loads carried on the hooves and hair of wild red deer (Cervus elaphus) to evaluate their epidemiological potential. Swab samples were collected from the hooves and hair of harvested deer in the Czerwony Bór Forest District, Poland. Quantitative PCR (qPCR) assays targeting the ITS1 region were deployed to detect total fungal DNA, Alternaria alternata, Fusarium avenaceum/F. tricinctum, and several Phytophthora species. A linear mixed-effects model was implemented to statistically evaluate variations in pathogen loads across anatomical sampling locations while controlling for individual animal variability. Fungal DNA was detected in 87.5% of hoof samples, showing significantly lower Ct values (13.85–18.54) compared to fur samples (17.02–29.56), which exhibited a more patchy distribution (p = 0.016). Similarly, A. alternata transfer was highly favored by hooves (p < 0.001). Conversely, F. avenaceum was more frequently detected on hair. Among oomycetes, Phytophthora pseudosyringae was detected in all sampled animals, whereas Phytophthora cactorum occurred rarely, and other tested Phytophthora species were not detected. Wild deer carry DNA of multiple fungal and oomycete pathogens and may act as potential passive carriers within forest ecosystems. Hooves constitute the primary vector for soil-borne pathogens due to sustained contact with topsoil, whereas hair facilitates the movement of specific canopy or airborne taxa. These findings suggest that wildlife movements should be considered in future forest biosecurity assessments for comprehensive forest health management and for understanding pathogen exchange between forest and agricultural ecosystems. Full article

Tomicus pilifer is an important wood-boring forest pest in China, and its host localization and intraspecific communication rely on the perception of volatile chemical cues. However, the molecular mechanisms underlying odor recognition in this species remain largely unknown. In this study, we systematically investigated the behaviorally active volatiles present in the hindgut and feces of T. pilifer and elucidated the roles of odorant-binding proteins (OBPs) in their recognition. Gas chromatography–mass spectrometry (GC–MS) identified eight volatile compounds common to both hindgut and fecal samples. Among them, five terpenoid compounds, α-pinene, 3-carene, D-limonene, camphene, and β-myrcene, elicited significant electroantennogram (EAG) responses and induced positive behavioral attraction in adults. Based on antennal transcriptome data, phylogenetic relationships with functionally characterized homologous OBPs, preliminary molecular docking analyses, and tissue-specific expression patterns, three candidate OBPs (TpilOBP5, TpilOBP16, and TpilOBP29) were selected from 51 identified TpilOBP genes and subsequently expressed as recombinant proteins. Fluorescence competitive binding assays demonstrated that all three OBPs bound to the five behaviorally active terpenoid volatiles, with TpilOBP29 exhibiting the broadest ligand-binding spectrum and the highest binding affinity. Molecular docking and interaction analyses further revealed that the binding pocket of TpilOBP29 forms a continuous hydrophobic core composed of multiple conserved hydrophobic residues, which cooperatively stabilizes ligand binding through hydrophobic interactions, π–alkyl interactions, and van der Waals forces, thereby conferring broad-spectrum and high-efficiency odorant recognition. These findings provide new insights into the molecular mechanisms underlying the recognition of key behaviorally active terpenoid volatiles in T. pilifer, identify TpilOBP29 as a key mediator of odor recognition, and provide a potential molecular target for the development of environmentally friendly semiochemical-based behavioral management strategies against bark beetle pests. Full article

High-precision estimation of forest aboveground biomass (AGB) is crucial for global carbon cycle monitoring and sustainable forest management. However, existing machine learning-based approaches often suffer from high-dimensional feature redundancy, uneven spatial distribution of training samples, and inefficient hyperparameter optimization, which collectively limit estimation accuracy and computational efficiency. To address these issues, this study proposes a synergistic feature-sample optimization framework (DFS) for high-precision forest AGB estimation. First, with the involvement of forestry experts, we constructed the Hunan and Hubei datasets covering typical subtropical forest types through multi-source remote sensing and ground plot sampling. Second, we propose the Dual-Criteria Adaptive Feature Selection (DCAFS) method, integrating ReliefF and mutual information criteria to adaptively select key features highly correlated with AGB, eliminating spectral redundancy while preserving biomass-sensitive information. Next, we introduce a Bidirectional Active Learning Sample Optimization mechanism, called BALSO, and in its forward step, plots with high uncertainty and representativeness are given priority, so samples with high AGB variability can be captured effectively; in the backward step, spatially redundant samples and feature-redundant samples are removed through density peak clustering, and by doing this, sample selection and spatial distribution are optimized at the same time, so plot balance gets improved. Finally, the framework brings in a parameter tuning structure based on Dream Optimization Algorithm, namely DOA, and through staged exploration together with local fine-tuning, DOA makes model hyperparameters and AGB data distribution characteristics align in an adaptive manner, which helps improve convergence efficiency and estimation stability. Input variables comprise Landsat 8 OLI spectral bands, GLCM texture features, vegetation indices, and Sentinel-1/2 data. On the Hunan dataset, the framework achieved an $R^2$ of 0.83 and an RMSE of 25.6 Mg·ha⁻¹; on the Hubei dataset, it achieved an $R^2$ of 0.86 and an RMSE of 26.8 Mg·ha⁻¹. The framework was further validated on an independent public dataset from Inner Mongolia. These results demonstrate that the DFS framework provides an effective and feasible approach for regional-scale forest AGB estimation and carbon monitoring. Full article

This study investigates the spatiotemporal dynamics of Land Use/Land Cover (LULC) along precipitation gradients across Türkiye by integrating high-resolution satellite-based precipitation datasets (CHIRPS, TerraClimate, and TRMM) with the European Space Agency (ESA) WorldCover (10 m) product and multi-sensor Normalized Difference Vegetation Index (NDVI) composites (Landsat, MODIS, Sentinel-2). Türkiye’s heterogeneous climate, characterized by a sharp contrast between humid coastal belts and semi-arid interiors, serves as a natural laboratory to assess ecosystem responses to moisture availability. The results reveal a systematic and non-linear transformation of LULC classes as precipitation increases. In low-rainfall zones (200–400 mm), agricultural activities and bare surfaces predominate, reflecting human-induced land management in water-constrained environments. A critical ecological threshold was identified between 400 mm and 700 mm, where grassland areas expand rapidly, becoming the dominant class. Beyond the 900 mm isohyet, forest cover exhibits a sharp increase, approaching nearly 100% dominance in regions exceeding 1200 mm, effectively displacing other LULC categories. Comparative analysis of precipitation products shows that while all datasets capture the “coastal-wet/inland-dry” pattern, TRMM tends to overestimate winter precipitation (exceeding 100 mm), whereas CHIRPS and TerraClimate provide more conservative estimates (75–80 mm). Overlay analyses between seasonal NDVI and precipitation confirm a pronounced “time-lag effect” in vegetation phenology. Despite peak precipitation occurring in winter (~75 mm), NDVI reaches its minimum (~0.03) due to thermal limitations and dormancy. Conversely, vegetation greenness peaks during the dry summer months (NDVI ~0.14 to 0.40), utilizing antecedent soil moisture stored during the spring recharge. High-resolution Sentinel-2 data proved superior in delineating micro-topographic vegetation responses compared to Landsat and MODIS. These findings provide a scientific baseline for sustainable land management and climate adaptation strategies, highlighting that precipitation thresholds are the primary determinants of Türkiye’s ecological boundaries. Full article

Excessive use of chemical fertilizers degrades soil health and threatens sustainable crop production, which can be mitigated by partially substituting chemical fertilizers with Chinese milk vetch (Astragalus sinicus L., MV, as green manure). However, the mechanisms through which MV incorporation alters soil microbial community structure and function, enhances soil quality and crop productivity, as well as its long-term effects in paddy soils, are still not fully understood. In this study, we investigated the responses of soil physical, chemical, and biological properties (to comprehensively evaluate soil quality); microbial community structure and function; rice productivity; and the sustainable yield index (SYI) to five fertilizer treatments based on a 13-year field experiment in a paddy’s soil in Henan, China. The treatments included: CK (no chemical fertilizer and no MV), F100 (100% chemical fertilizer), MVF80, MVF60 and MVF40 (80%, 60%, and 40% of the chemical fertilizer rate combined with MV, respectively). Compared with the F100 treatment, MVF60 slightly increased rice yield by 1.71% and significantly improved SYI by 5.10%. All MV treatments significantly increased soil organic carbon (SOC, by 14.4–16.3%) and microbial biomass carbon (MBC, by 16.7–20.1%). MVF60 and MVF40 significantly reduced bulk density, and increased macroaggregate content and mean weight diameter (MWD). MVF80 significantly enriched soil total phosphorus (TP), total potassium (TK), mineral nitrogen (Nmin), and urease (UE). The improvement in these soil properties resulted in a marked increase (by 11.6–20.1%) in the soil quality index (SQI) under all MV treatments. Random forest analysis identified MBC and Nmin as the most important predictors of SQI. Moreover, MV incorporation increased the relative abundance of beneficial taxa (Firmicutes, Clostridium_sensu_stricto_1, Bradyrhizobium, and Nigrospora), which were positively correlated with SQI (p < 0.05), while reducing the relative abundance of pathogenic fungal genera such as Fusarium. Furthermore, regression analysis revealed strong positive correlations between SQI and both rice yield and SYI. In summary, long-term MV incorporation with a 40% reduction in chemical fertilizer (MVF60) constitutes an effective and sustainable nutrient management approach for rice production in southern China. This practice enhances soil quality through improved physical structure, nutrient cycling, and microbial community structure and function, ultimately resulting in higher and more stable yields. Full article