Search Results (1,007)

Maize production in the black soil region of Northeast China is highly vulnerable to drought and flood stress, yet stage-specific mechanisms under rain-fed conditions remain unclear. Daily meteorological records from 1951 to 2024 were used to calculate the Crop Water Surplus Deficit Index (CWSDI) for four maize phenological stages, and 2025 in situ soil moisture and temperature observations were used to derive root-zone soil water storage (SWS), soil water depletion rate (SWDR), and the soil temperature–moisture coupling index (STMI). The growing season showed a persistent water deficit (mean CWSDI = −39.19%). Drought risk was greatest during sowing–jointing (S1; CWSDI = −64.73%; drought frequency = 73.0%) and milk–maturity (S4; CWSDI = −49.84%; drought frequency = 58.1%), whereas jointing–tasseling (S2) had the highest flood frequency (13.5%). Soil hydrothermal indicators showed that S1 drought was evaporation-driven, S2 involved potential hot-wet compound stress, tasseling–milk (S3) had rapid root-zone water depletion, and S4 drought was driven by insufficient late-season precipitation. These findings show that maize water stress is a sequence of stage-specific mechanisms rather than a uniform seasonal phenomenon. We therefore propose a regulation strategy combining soil moisture conservation, rainwater harvesting, precision supplemental irrigation, and field drainage to improve maize resilience. Full article

Understanding how soil arthropod communities respond to nutrient enrichment is important for assessing grassland ecosystem health, but such knowledge remains limited for degraded Sophora alopecuroides grasslands. To address this gap, a two-year field experiment was conducted in the Tuhulusu grassland (Xinjiang, China) with four treatments: nitrogen (N) addition, phosphorus (P) addition, combined N and P (NP) addition, and an unamended control (CK). Soil arthropod communities and environmental variables were monitored during the flowering, maturity, and senescence stages of S. alopecuroides. Across all treatments, three taxa—Oppiidae, Hypoaspidae, and Rhagidiidae—remained dominant, indicating wide ecological tolerance. Nutrient addition significantly altered arthropod individual density (response variable) and soil properties, including total phosphorus, available phosphorus, nitrate−N, ammonium−N, and pH (all p < 0.001), and these effects were strongly linked to plant phenology. The dominance, evenness, and Shannon diversity indices ranked as NP > CK > P > N. The key environmental drivers varied by treatment: total phosphorus and soil moisture under N addition, soil moisture under P and NP addition, and pH and electrical conductivity under CK. Collectively, these findings provide evidence that soil arthropod communities in S. alopecuroides grasslands are sensitive to nutrient enrichment in a phenology−dependent manner, with soil moisture content emerging as a critical limiting factor under nutrient−added conditions. Full article

Forest soils serve as critical terrestrial carbon sinks. While broad hydrothermal controls on soil respiration (R_s) are established, uncertainties persist regarding high-frequency temporal dynamics and moisture-dependent variations in temperature sensitivity (Q₁₀). Specifically, conventional reliance on discrete, clear-day sampling obscures how precipitation disrupts diurnal patterns. To address this, we continuously monitored R_s and environmental factors in two Northeast Chinese mixed forests (Korean pine, Pinus koraiensis (KP), and Dahurian larch, Larix gmelinii (DL)) to quantify weather-driven daily dynamics and carbon fluxes. Precipitation primarily drove daily variability, but more importantly, it reshaped day–night asymmetry. Under clear-day conditions, R_s exhibited a consistent daytime-dominant pattern, with daytime fluxes being significantly higher than nighttime fluxes (p < 0.05). However, precipitation events fundamentally neutralized this asymmetry, resulting in no significant day–night differences across most phenological stages. Annual R_s effluxes (759 and 965 g C m⁻² yr⁻¹ for KP and DL, respectively) lacked significant inter-stand or temporal variations. Seasonal emissions peaked unimodally in July, with the non-growing season contributing merely 5%–8%. Notably, spring freeze–thaw R_s in the KP stand surged interannually by 143%. While R_s correlated positively with temperature (p < 0.001), Q₁₀ was co-regulated by forest stand and moisture. Under moderate moisture, the KP stand’s Q₁₀ (2.72) was significantly lower than the DL stand’s (3.81); however, this divergence neutralized under low moisture. Consequently, soil moisture acts as both a direct R_s driver and a fundamental regulator of its temperature sensitivity. These empirical findings provide critical data to calibrate forest carbon models, improving predictions of soil carbon feedbacks under future climate scenarios. Full article

Optimizing orchard mulching regimes is a pivotal strategy for mitigating the detrimental effects of water scarcity and soil degradation on kiwifruit productivity in the Guanzhong Plain, China. To characterize the integrated effects of varying mulching patterns, a two-year field study was conducted in a kiwifruit (Actinidia deliciosa) orchard, evaluating four treatments: (1) FG: intra-row fabric with inter-row grass (multiple mulch); (2) FN: intra-row fabric with inter-row bare soil; (3) NG: intra-row bare soil with inter-row grass; and (4) NN: intra-row bare soil with inter-row bare soil. Understanding the impacts of these regimes on the edaphic environment, photosynthetic performance, and sugar metabolism is essential for improving kiwifruit production under semi-arid conditions. The results demonstrated that the FG treatment significantly improved soil water storage (SWS), with an increase of 1.83–55.16 mm, and enhanced the soil nutrient content (NH₄⁺-N, NO₃⁻-N, and soil organic matter), thereby optimizing the rhizosphere environment. During the critical phenological stages, the FG treatment increased the leaf photosynthetic parameters, such as the net photosynthetic rate (Pn), transpiration rate (Tr), and stomatal conductance (Gs), while reducing the intercellular CO₂ concentration (Ci). Specifically, grass mulching (FG and NG) elevated the chlorophyll a content during early growth and carotenoids levels throughout reproduction, whereas fabric mulching (FG and FN) enhanced the chlorophyll b content throughout the entire reproductive period. Collectively, these improvements bolstered photosynthetic efficiency and may have contributed to improved carbon allocation and sugar accumulation. All three mulching treatments (FG, FN, and NG) significantly improved the fruit yield-related parameters, including the total fruit number per plant (PFN), single fruit weight (SFW), and yield (Y), as well as the fruit sugar-related indices, such as soluble solids content (TSS), total soluble sugar content (TS), reducing sugar (TRS), and the sugar–acid ratio (SAR). The partial least squares path modeling (PLS-PM) revealed that these improvements were primarily driven by the synergistic optimization of SWS and photosynthetic productivity. Notably, the model identified a physiological trade-off between yield formation and sugar accumulation, while the overall fruit quality exerted a strong positive influence on sugar metabolism. The correlation analysis indicated that the higher fruit sucrose accumulation under the FG and FN treatments were associated with increased sucrose phosphate synthase (SPS) and sucrose synthase (SS) activities, suggesting a potential link between mulching-induced improvements in plant physiological status and sucrose metabolism. These findings suggest that the combined use of intra-row fabric and inter-row grass mulching (FG) provides a sustainable strategy for enhancing soil conditions and fruit quality in water-limited kiwifruit orchards. Full article

With global climate change, heatwaves have become more frequent and severe in avocado-growing regions. High temperatures combined with wind and low humidity are problematic for avocados, especially during the early developmental stage of the young fruitlets. Hence, heatwaves during this phenological stage are considered a major limiting factor for avocado productivity. This study evaluated the effects of operating pulsing sprinklers or sprayers installed above the canopy during spring heatwaves over three consecutive seasons in a Hass avocado orchard. We evaluated foliage and fruitlet temperature (using remote and proximal sensing), stem water potential, stomatal conductance, salt accumulation on the leaves, and productivity. The cooling system reduced the foliage temperature by 6–8 °C and fruitlet temperature by 5–10 °C with respect to uncooled trees. Stem water potential was increased by 0.8–2.0 MPa in the treatment plots compared to the control. The cooling treatments led to an average 42% yield increase over the next 3 years. No significant differences were found between the sprinklers and sprayer for any of the measured parameters. Results indicate the effectiveness of an evaporative cooling system for mitigating heatwave damage and improving avocado productivity. Full article

Urban trees are increasingly exposed to persistent anthropogenic drivers that extend beyond climatic forcing and fundamentally alter the conditions of secondary growth. While climatic controls of cambial phenology and xylogenesis are well established, the mechanisms by which non-climatic drivers regulate cambial activity and wood formation remain fragmented and are often inferred only indirectly. Here, we develop a cambium-centred framework to synthesise current evidence on how anthropogenic drivers shape wood formation in urban and peri-urban trees. To our knowledge, this is among the first syntheses explicitly linking anthropogenic drivers to distinct stages of xylogenesis. Anthropogenic drivers are typically chronic, spatially heterogeneous, and temporally decoupled from seasonal climatic rhythms, and may alter cambial kinetics and generate anatomical signatures not captured by ring width alone. We evaluate major driver domains, including root-zone constraints, altered hydrology, urban microclimate, pollution, salinity, and mechanical disturbance, while also considering emerging drivers such as artificial light at night and microplastics. Evidence is stratified into three levels: direct observations, indirect physiological evidence, and mechanistic plausibility. Across driver classes, three recurrent anatomical patterns emerge: reduced conduit size under hydraulic or osmotic stress; anomalies in wall deposition under carbon limitation or oxidative stress; and pronounced circumferential heterogeneity under spatially localised forcing. Integrative approaches combining xylogenesis monitoring, quantitative wood anatomy, dendrometer observations and spatially explicit sampling are essential to disentangle anthropogenic from climatic effects and improve assessment of tree resilience. Full article

Accurate field-scale wheat yield estimation is essential for precision agriculture, farm-level decision-making, and food security planning. However, operational studies conducted under real commercial farming conditions in the eastern Mediterranean remain limited. This study investigated whether multi-temporal Sentinel-2 imagery could support reliable wheat yield estimation across nine commercial wheat fields near Ptolemaida, Greece, during the 2023–2024 growing season. Both durum and common wheat fields were included, and combine-harvester yield maps were used as ground-truth observations. Six regression algorithms—the Random Forest (RF), Support Vector Regression (SVR), k-nearest neighbors (KNN), Decision Tree (DT), LASSO regression, and Gaussian Process Regression (GPR) algorithms—were evaluated using three feature configurations: raw Sentinel-2 spectral bands only (Sentinel-only (SO)), spectral bands combined with vegetation indices (Sentinel+Indices, SI), and vegetation indices only (Indices-only, IO). Model generalization was assessed through a strict Leave-One-Field-Out (LOFO) cross-validation protocol, and the method of SHapley Additive exPlanations (SHAP) was used to interpret model behavior and identify the most influential spectral regions and phenological stages. RF achieved the highest predictive accuracy, with a MAPE of 7.90% and an RMSE of 45.15 kg decare⁻¹ under the SO configuration, demonstrating a statistically significant improvement over DT and KNN models ($p<0.05$). SHAP analysis indicated that model predictions were mainly driven by SWIR-1, NIR-narrow, and red-edge bands acquired during late grain filling and maturity, while vegetation indices contributed limited additional information. These findings suggest that raw multi-temporal Sentinel-2 spectral bands are highly effective for field-scale wheat yield estimation within the scope of this study, although further validation across diverse growing seasons and geographic regions is required to confirm broad operational sufficiency. Full article

The response of tree growth to environmental (climatic) changes has largely been analyzed through ring width–climate relationships, yet such analyses often lack the dynamic process of radial growth in response to environmental changes. Therefore, this study focuses on Korean pine (Pinus koraiensis Siebold & Zucc.) and white birch (Betula platyphylla Sukaczev) at three elevations (750 m, 950 m, and 1150 m) in the broadleaved Korean pine forest on the northern slope of Changbai Mountains, China. We systematically monitored cambial activity and the dynamics of xylem formation stages to analyze the different adaptation strategies of the two species in terms of phenology, cellular characteristics, growth rates, and climatic responses during cambial and xylem formation stages. The results showed that the phenological stages of xylem formation in Korean pine were more sensitive to elevation, while the phenological changes in birch were smaller, indicating greater growth stability. The seasonal dynamics of the number of xylem cell layers in both species followed a unimodal or sigmoid curve, but high elevations significantly inhibited the number of mature cell layers. Gompertz model fitting revealed that the maximum growth rate of Korean pine decreased significantly with increasing elevation, whereas no significant change was observed in birch. With increasing elevation, temperature emerged as the primary factor influencing cambial phenology and growth duration in both species, while precipitation dominated changes in growth rates. Xylem growth in Korean pine was co-regulated by growth rate (R² = 0.62) and growth duration (R² = 0.35), with tracheid diameter closely related to the duration of expansion (R² = 0.36). The regulatory pattern of xylem growth in birch was similar to that in Korean pine but with weaker correlations. In summary, Korean pine, as a coniferous dominant species, is more sensitive to temperature changes induced by elevation and adapts to elevational variations by adjusting phenology and cell development. In contrast, birch, as a broadleaved pioneer species, exhibits a high buffering capacity in xylem formation in response to elevational changes, thereby maintaining growth stability. The divergent growth strategies of the two species reveal the potential response pathways of temperate forest tree species to environmental changes and provide important insights for predicting the dynamics of broadleaved Korean pine forests. Full article

This study evaluated the effects of abiotic factors and extraction conditions on the yield, chemical composition, and antimicrobial activity of essential oil (EO) from Pistacia lentiscus L. leaves collected at four Adriatic locations during three phenological stages. Steam distillation was performed at 0.3, 0.7, and 1 bar. EO yield increased significantly with pressure, reaching a maximum at 1 bar, while the flowering stage provided the highest yields overall. Leaves from Vela Luka produced the highest EO yield, whereas Pag samples yielded the least. GC–MS analysis identified 56 components, accounting for 99.19–99.99% of total EO, with α-pinene, limonene, myrcene, and β-pinene as the dominant constituents, confirming a monoterpene-rich chemotype. All EO samples showed low but measurable inhibitory activity against Escherichia coli AB1157 and Erwinia amylovora EaED, as assessed by the disk diffusion method. Pearson correlation and PCA analyses indicated a positive association between monoterpene content and inhibition zone diameter against E. coli, and a positive association between monoterpene alcohol content and inhibition against E. amylovora. As antimicrobial activity was assessed exclusively by the disk diffusion method, the present findings may serve as an indicative basis for future investigations into the relationship between EO chemical composition and antimicrobial potential, and they require validation through quantitative, standardized antimicrobial testing. Full article

Precise mapping of rice cultivars is of great significance for crop management and food security evaluation. Nevertheless, differentiating between Indica and Japonica rice remains a formidable task, mainly due to subtle discrepancies in spectral characteristics and scattered planting distributions. This study evaluated the synergistic effect of spatial resolution and red edge information in rice variety classification using PlanetScope (PS) and Sentinel-2 (S2) images from the Tillering and Jointing stage, Heading and Flowering stage in Huai’an, Jiangsu Province. Multiple feature schemes were constructed, including spectral bands, vegetation indices, and texture features, with and without red-edge variables. A total of eight feature schemes have been constructed, including spectral bands, vegetation index, texture features, and red edge features. The feature scheme division is based on the participation of different sensors, growth periods, and red edges. We fine-tune three classification models, Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and TabNet, to enhance classification performance. Additionally, we employ Shapley Additive Explanations (SHAP) to quantitatively measure the contribution of each feature to the prediction of distinct rice varieties. Results demonstrate that classification accuracy of different sensors reach the highest at the Heading and Flowering stage. The overall accuracy of PS scheme is 98.14%, the F1 scores of Japonica and Indica rice are 97.67% and 98.41%, the overall accuracy of S2 scheme is 97.87%, and the F1 scores of Japonica and Indica rice are 98.62% and 98.68, respectively. Incorporating red-edge features leads to a notable improvement in F1-scores for both Indica and Japonica rice under all experimental configurations. Although PS only has one red edge band set, its classification performance is similar to S2, and the boundaries between different rice variety recognition results and between non rice and rice plots are more refined compared to S2. Feature attribution analysis reveals that red-edge indices exert a dominant influence on the decision-making process of the models, especially during the Heading–Flowering period. These findings suggest that high-accuracy discrimination of rice varieties relies heavily on the synergistic optimization of phenological timing, red-edge spectral information, and spatial resolution, rather than merely increasing spectral dimensionality. The optimization direction for high-precision rice variety mapping in the future should prioritize the collaborative mechanism of phenological period, red edge data, and spatial resolution, rather than being limited to simple stacking in the spectral dimension. Full article

The durian seed borer, Mudaria stahlgretschae, is a major economic pest that has significantly impacted durian cultivation in Southeast Asia; however, comprehensive biological and ecological data for this species remain limited. This study employs an integrative taxonomic approach, combining morphological examination with molecular validation of the mitochondrial cytochrome c oxidase subunit I (COI) gene. Phylogenetic analysis (Neighbor-Joining) confirmed that all collected specimens (n = 11) formed a distinct monophyletic clade within the genus Mudaria, showing a genetic identity of 95.75–96.85% with existing GenBank accessions, thereby confirming their identity as M. stahlgretschae. Systematic monitoring using light traps in Uttaradit Province revealed a clear seasonal phenology, with adult flight activity restricted to a five-month period from April to July 2025. Population density peaked in May (55.56%), synchronized with the mid-stages of durian fruit development. Furthermore, chemical profiling of female gland volatiles via GC-MS identified 40 compounds; among these, four putative sex pheromone candidates—1-Hexacosene, (Z)-7-Hexadecenal, 11-Octadecenal, and 2-Hexadecanol—were identified as key constituents based on their consistent detection across all replicates (n = 3), high relative abundance, and absence in male extracts or blank controls. These findings establish a critical foundation for developing synthetic pheromone lures and synchronized monitoring programs, offering a robust framework for the sustainable management of M. stahlgretschae in durian agroecosystems. Full article

Pea (Pisum sativum L.) is an important temperate grain legume crop of high nutritional and agronomic value. Ascochyta blight, caused by a multi-species complex of necrotrophic fungi, remains a major constraint for pea production worldwide. This review synthesizes the available genetic, physiological and molecular knowledge on the pea–Ascochyta blight pathosystem, with emphasis on the genetic architecture of resistance, host defense mechanisms and the recent contributions from the omics disciplines. Current evidence indicates that genetic resistance to the various Ascochyta blight pathogens is incomplete and multicomponent, being associated with loci of small to moderate effect, with expression depending on organ, developmental stage and environment. Under field conditions, the observed phenotypes reflect the interaction between physiological resistance, plant architecture, phenology, canopy microenvironment and epidemic dynamics. Together, these factors bias phenotyping and limit the transferability of molecular markers. The practical value of these markers for use in marker-assisted selection (MAS) and genomic selection (GS) is presented and critically discussed. Future progress in breeding for Ascochyta blight resistance will depend on integrating molecular knowledge with a careful definition of ideotypes, well-calibrated phenotyping and multi-environment validation. Full article

Population growth, climate change, and increasing pressure on water and nitrogen resources pose major challenges for sustainable maize production. Maize yield is highly sensitive to inter-annual weather variability, yet many prediction approaches still rely on simple linear relationships and rarely integrate SPAD (Soil Plant Analysis Development)-based crop diagnostics with machine learning in multi-year nitrogen × irrigation experiments. In a three-year field experiment (2018–2020) in Hungary, we evaluated how basal and top-dressing fertilization and supplemental irrigation under contrasting water supply conditions affected the chlorophyll status and grain yield of a maize hybrid. Relative chlorophyll content was monitored using SPAD measurements at key phenological stages (V6, V12, and R1), and a multilayer perceptron (MLP) model was developed to improve yield prediction and to identify informative combinations of input variables. Five alternative scenarios (SC1–SC5) were tested by combining SPAD values with the fertilization rate, irrigation status, and crop year in different configurations, and model performance was assessed using root mean square deviation (RMSD), mean absolute error (MAE), normalized root mean square error (NRMSE), correlation (r, r²), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), Kendall’s tau, and the index of agreement (d). Overall, SC4 (SPAD + fertilization + crop year + irrigation) achieved the best agreement with observed yields across most indices (e.g., r ≈ 0.93, NSE ≈ 0.86, KGE ≈ 0.90), whereas SC2 (SPAD + fertilization) produced the lowest prediction error on the independent test subset, indicating the most robust generalization. Basal fertilization with 60 and 120 kg N ha⁻¹ significantly increased yield in 2019 and 2020, while irrigation generally enhanced yield except for the 30 kg N ha⁻¹ top dressing applied at the V6–V12 stages. These results demonstrate that coupling SPAD measurements with MLP modeling and multi-criteria performance evaluation can support more efficient, site-specific nitrogen and irrigation decisions and help stabilize maize yields under variable climatic conditions. Full article

Plant genetic resources are increasingly viewed as a key tool to address the multiple challenges faced by modern viticulture. In this context, local grape cultivars are proposed as a strategy to enhance resilience to climate change and to diversify wine styles. However, while genetic identification has been widely reported, field-based phenotyping information for local cultivars under current climate conditions remains limited. In this context, phenotyping results are presented for six local Andalusian cultivars (Castellano, Beba, Cañocazo, Mantúo de Pilas, Perruno and Vigiriega). All cultivars were grown in a vineyard plot in the Marco de Jerez and evaluated over three consecutive seasons (2023–2025). Morphology was assessed using 46 descriptors, allowing cultivars to be grouped into two main clusters. Phenological monitoring showed a measurable year effect while preserving a consistent relative ranking among cultivars, most clearly during veraison and ripening, with Castellano reaching these stages earlier and Mantúo de Pilas later. Grape must composition highlighted contrasting ripening dynamics, with Palomino Fino and Castellano generally reaching higher sugar levels, whereas Vigiriega and Mantúo de Pilas showed the most acidic profiles. These results provide growers with performance-based comparative information to support cultivar selection for new plantings and to explore the potential of local cultivars for developing new wine styles under warm climate conditions. Full article

Aiming at the dual dilemma in high-resolution cropland change detection, where CNNs are constrained by limited local receptive fields and Transformers suffer from heavy computational costs, we propose LiteScan-Net, a lightweight and robust network architecture incorporating scanning principles from state-space modeling. The network innovatively introduces the Multi-Directional Global Scanning (MDGS) mechanism as an efficient engineering surrogate, which simulates the selective scanning process using large-kernel 1D convolutions. This achieves global context modeling with linear complexity while avoiding the hardware limitations imposed by recurrent computations. Based on this mechanism, a three-stage collaborative architecture is constructed: the Coordinate-Aware Feature Purification (CAFP) module is designed to mitigate shallow phenological noise via coordinate sensitivity; the Context Difference Verification (CDV) module aims to alleviate pseudo-changes caused by registration errors through global alignment; and the State-Space Guided Refinement (SSGR) module promotes the generation of change masks with precise boundaries and compact interiors. To verify the model generalization, we construct a Massive Specialized Cropland Change Detection dataset named MSCC, which exhibits significant cross-scale characteristics. Experimental results demonstrate that LiteScan-Net achieves state-of-the-art (SOTA) performance across the CLCD, Hi-CNA, and MSCC datasets, with F1-scores of 79.43%, 84.82%, and 89.62%, respectively. With a low computational cost of only 1.78 GFLOPs and a real-time inference speed of 37.9 FPS, LiteScan-Net demonstrates high potential for future deployment on resource-constrained edge devices. Full article