Search Results (642)

The Huaibei region is a critical grain and energy–chemical base in northern China, characterized by substantial water demand for industrial and agricultural production. Karst groundwater systems constitute the primary water supply source in this area. Under the superimposed impacts of intensive exploitation, climate change, and anthropogenic activities, karst aquifers have encountered a series of geo-environmental problems, including groundwater level decline and expansion of cones of depression. Most previous studies have predominantly focused on water quality assessment and groundwater resource quantification, yet systematic investigations into the multi-scale characteristics and driving mechanisms of karst groundwater level dynamics remain insufficient. In this study, based on long-term groundwater level and rainfall monitoring data (2014–2024) from three monitoring wells representing different types of karst aquifers, continuous wavelet transform (CWT) and wavelet coherence (WTC) approaches are introduced to identify the periodic patterns of karst groundwater levels and reveal the dominant controlling factors of groundwater level dynamics. The results demonstrate that groundwater levels in all types of karst aquifers exhibit distinct multi-scale periodic variations. The groundwater levels of HB01 and HB02 share dominant oscillation periods of 18~19 months and 9 months with regional rainfall, while the groundwater level at HB03 displays a more complex, multi-scale, periodic combination of 41 months, 18~19 months, and 9 months. Periodic variations in regional rainfall serve as the dominant controlling factor for the intra-annual and inter-annual periodic fluctuations of karst water levels, with a prominent resonance relationship identified between the two variables at dominant periodic scales. Distinct heterogeneity is observed in the response magnitude and lag time of different karst aquifer types to rainfall; specifically, the lag time of water level response to rainfall on the annual periodic scale ranges from 2.7 to 2.9 months. The correlation between annual average water level and pumping discharge is moderate for boreholes HB01 and HB03, whereas a strong correlation is detected for borehole HB02, implying that its water level regime is likely subjected to pronounced pumping disturbance. The degree of karst development, aquifer burial depth, and overlying stratum architecture are the key geological factors accounting for such heterogeneous response patterns. For the first time, this study utilizes long-term water level time series data from the karst water exploitation zone of the Huaibei Plain, complemented by synchronous precipitation and pumping records. Integrated with regional hydrogeological settings, wavelet analysis is employed to conduct an in-depth investigation into the dynamic variations in karst water levels in the Huaibei region from the perspective of groundwater recharge–discharge relationships. The results provide a scientific underpinning for the remediation of karst water over-exploitation and the optimal allocation of water resources. Specifically, pumping and artificial recharge schemes can be proactively adjusted based on periodicity forecasts. Zoned management strategies for water resources are put forward: artificial regulation and storage are recommended for zones with sensitive hydrological responses, while preventive protection is prioritized for zones with sluggish responses. By incorporating periodic characteristics and lag durations, targeted pumping strategies for dry and wet seasons can be developed, and a coupled water level–rainfall–pumping early warning system can be established to realize the long-term sustainable regulation of karst water resources. Full article

The growing interest in natural bioactive compounds has positioned sea buckthorn (Hippophae rhamnoides L.) as a source of high-value phytochemicals for pharmaceutical and functional applications. Renowned for its complex biochemical profile, this species synthesizes significant levels of lipophilic and hydrophilic antioxidants, notably carotenoids and ascorbic acid (vitamin C). These constituents provide extensive therapeutic and pharmacological benefits, including strong antioxidant, anti-inflammatory, and tissue-regenerative properties. Consequently, characterizing the exact bioactive composition of sea-buckthorn is essential for identifying cultivars with exceptional multi-functional potential. However, a major challenge in exploiting this botanical resource lies in the significant variability of its chemical profile. While the fundamental pharmacological attributes of the plant are genetically determined, the absolute concentration of its bioactive compounds can vary. Factors such as specific cultivar traits and inter-annual climatic conditions, including severe seasonal droughts, are known to influence secondary metabolite accumulation. Although individual varieties exhibit distinct nutritional profiles, systematic data tracking these fluctuations over consecutive years remain limited, particularly regarding certified cultivars adapted to specific regional ecosystems. To extend current understandings, this study evaluates the multi-functional characteristics and inter-annual stability of 17 distinct sea buckthorn varieties over a continuous three-year monitoring period. Specifically, this research focuses on four select varieties officially registered in the Republic of Moldova Official Catalogue of Plant Varieties and Species. The primary aim of this investigation is twofold: first, to quantify key quality parameters—including carotenoid levels, vitamin C content, total acidity, pH, and dry matter—across the different cultivars; and second, to determine whether genetic variety or inter-annual climatic variations exert the dominant influence on these bioactive levels over the medium term. By evaluating these parameters, this study establishes reliable baselines for selecting stable, high-yield cultivars optimized for targeted pharmacological applications. Full article

Soybean (Glycine max L.) yield is a complex trait determined by genotype, yield components, and environment. This study hypothesized that genotype × environment interactions influence yield stability and that yield components show differential sensitivity to interannual climatic variation in Mediterranean environments. The objectives were to (i) evaluate 31 soybean genotypes across two seasons, (ii) identify stable, high-yielding genotypes, and (iii) determine the contribution of yield components to yield variation. Field experiments were conducted in Antalya, Türkiye, during 2020 and 2021. Plant height, first pod height, pods per plant, 1000-seed weight, and seed yield were recorded. Significant differences were found among genotypes, years, and their interactions for all traits (p < 0.001). Plant height and pod number were higher in 2020 (mean: 102.5 cm and 96.2 pods plant⁻¹), indicating favorable conditions for vegetative growth. In contrast, 1000-seed weight was markedly higher in 2021 (mean: 181.7 g vs. 81.3 g in 2020), suggesting enhanced seed filling under reduced sink demand. Seed yield ranged from 231 to 710 kg da⁻¹, with ACSOY119 producing the highest mean yield (592 kg da⁻¹), followed by ACSOY50 (562 kg da⁻¹) and ACSOY83 (548 kg da⁻¹). Pod number was the most environmentally sensitive component (r = 0.74 with yield), while increased seed weight partially compensated for yield losses. Yield was strongly correlated with pod number (r = 0.74, p < 0.01) and moderately with first pod height (r = 0.59, p < 0.01) and 1000-seed weight (r = 0.34, p < 0.05). Based on mean yield, ACSOY119 and ACSOY50 were identified as the highest-yielding and most stable genotypes. These findings emphasize multi-year evaluations and integrated component analysis for identifying high-yielding soybean genotypes with adaptability under Mediterranean conditions. Genotypes with consistent performance, such as ACSOY119 and ACSOY50, are recommended for breeding programs. Full article

Understanding the interactions between vegetation dynamics and surface water availability is essential for assessing the resilience of tropical floodplain ecosystems under increasing hydroclimatic variability. This study analyzes the spatio-temporal dynamics of vegetation cover, surface water, and land use in Zambrano, a floodplain-dominated sector of the lower Magdalena River basin (Colombian Caribbean), over the period 1990–2025. Multi-temporal Landsat imagery was used to derive the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), enabling the evaluation of seasonal and interannual ecohydrological variability under contrasting dry and rainy conditions. In addition, land-use classification was performed using a CORINE Land Cover methodology adapted for Colombia (CLC-C) to characterize the spatial organization of the landscape and its influence on vegetation–water interactions. Results show that vegetation dynamics are strongly controlled by hydroclimatic seasonality. Dense vegetation consistently expands during rainy periods, while dry seasons promote the expansion of open and sparse vegetation, reflecting seasonal vegetation stress rather than long-term degradation. NDWI patterns indicate that surface water and soil moisture are highly seasonal and spatially constrained, with open water largely confined to the Magdalena River channel and localized floodplain depressions. Extreme hydroclimatic events associated with the El Niño–Southern Oscillation (ENSO) produce abrupt but temporary changes in vegetation structure and surface moisture distribution. A strong inverse correlation between NDVI and NDWI reflects the contrasting spectral responses of vegetation and water surfaces resulting from the shared near-infrared (NIR) band in both indices. This spectral relationship is consistent with the observed seasonal variations in vegetation greenness and surface moisture across the floodplain. Land-use analysis reveals the progressive consolidation of the landscape, where the agropastoral matrix expanded from ~18,000 ha in 1990 to over 22,000 ha by 2025, driving a systematic reduction in natural and semi-natural forest structures. Forest conservation areas serve as critical ecological buffers, exhibiting lower seasonal variability in vegetation greenness. Overall, the results indicate that the Zambrano floodplain functions as a structurally stable yet highly responsive ecohydrological system, where vegetation dynamics and surface water availability are predominantly governed by interannual hydroclimatic pulses rather than long-term directional degradation. These findings demonstrate that while the structural matrix of the floodplain exhibits strong baseline resilience, its ecological functioning remains critically coupled with, and vulnerable to, the extreme phase shifts in ENSO cycles. Full article

The increasing interest in natural versus synthetic additives is a driving force for food industry innovation to develop alternative solutions for food safety and quality. Brown algae, described as containing potential antimicrobial and antioxidant compounds, are promising alternative sources. However, optimized food-grade extracts require preserving their bioactivity. In this study, the effects of seasonality and interannual variation, together with non-thermal high-hydrostatic-pressure (HHP) pretreatment, on the extraction of antioxidant and antimicrobial compounds from the macroalga Ericaria selaginoides, collected from the northwest of Spain between November 2021 and September 2023, were assessed. The HHP pretreatment of fresh algae did not significantly change the yield of the crude extracts and only slightly improved the antimicrobial activity of the extracts against L. monocytogenes, S. aureus and B. cereus, without significantly diminishing the antioxidant activity until 600 MPa for 5 min, the total polyphenol content (TPC), and the chlorophyll A content. Interannual and seasonal variations significantly influenced pigments and protein, carbohydrate and polyphenol contents, together with the antioxidant and antimicrobial activities, of the extracts. By NMR, phloroglucinol was identified as the major secondary metabolite, together with mannitol, alanine and a mixture of meroditerpenoids, which may contribute to the reported activities. The overall results demonstrated the role of environmental factors in driving seasonal and interannual changes in macroalgal metabolism, significantly influencing the bioactive properties of extracts, while the effect of HHP pretreatment was minimal. Full article

Following the 2021 Montiferru wildfire, one of the largest wildfire events in modern Italian history, assessing the suitability of olive-growing environments for high-quality extra virgin olive oil (EVOO) production is crucial for supporting sustainable agricultural restoration. This study models the spatial distribution and temporal stability of total polyphenol concentration in EVOO (cv. Bosana) across a complex Mediterranean landscape. Olive samples from georeferenced sites were collected during the 2022 (22 samples) and 2023 (37 samples) harvest seasons and processed using a standardized protocol. Spatial modeling was performed via Empirical Bayesian Kriging Regression Prediction (EBKRP), integrating seven bioclimatic and topographic covariates. Cross-validation demonstrated high predictive accuracy with negligible bias (RMSE = 53.3 and 58.8 mg kg⁻¹ for 2022 and 2023, respectively). While single-predictor correlations were weak, multi-variable analysis highlighted a strong interaction between topography and water balance driving phenolic accumulation. The mean prediction map identified regional hotspots approaching600 mg kg⁻¹ of polyphenols content in the obtained olive oils. Spatial overlay analysis successfully delineated “Stable High-Phenolic Core Areas” (>500 mg kg⁻¹ with interannual variation < 100 mg kg⁻¹), filtering out high-altitude marginal zones. This geostatistical approach provides a valuable territorial decision-making tool to support post-fire agricultural reconversion and the valorization of high-quality monovarietal EVOO terroirs. Full article

Long-term ecological monitoring is essential for understanding biodiversity dynamics and evaluating conservation effectiveness in protected areas. The Baishuihe National Nature Reserve, located in the southern Minshan Mountains, represents an important montane forest ecosystem within a global biodiversity hotspot. Using camera-trapping data collected from 2011 to 2020, we investigated long-term community dynamics and temporal activity patterns of mammals and ground-dwelling pheasants in the reserve. A total of 23 species, including 17 mammal and 6 pheasant species, were recorded during the 10-year survey. Carnivora and Artiodactyla were the most species-rich orders, while several ungulates and pheasants dominated detections throughout the monitoring period. Species richness increased with survey duration, while the rate of newly recorded species declined after 2016, and the observed assemblage became more consistent across years. Interannual community variation reflected both species turnover and nestedness-related processes, although dominant and common species remained consistently detected across years. Camera-trap data further revealed pronounced temporal niche differentiation among sympatric species. Carnivores, such as leopard cat (Prionailurus bengalensis) and masked palm civet (Paguma larvata), were primarily nocturnal, whereas golden snub-nosed monkey (Rhinopithecus roxellana), Temminck’s tragopan (Tragopan temminckii), and blood pheasant (Ithaginis cruentus) showed predominantly diurnal activity patterns. Several species exhibited broad temporal niches or seasonal shifts in activity rhythms, suggesting flexible behavioral adaptation to environmental conditions. Overall, the Baishuihe Reserve supported a taxonomically and functionally diverse camera-trap-detectable vertebrate assemblage, within which dominant and common species showed relatively persistent occurrence across years. This study highlights the importance of long-term camera-trap monitoring for understanding community stability, temporal niche organization, and biodiversity conservation in mountain ecosystems. Full article

The Agulhas Current system, the strongest western boundary current in the Southern Hemisphere, plays a key role in regulating carbon cycling in the southwestern Indian Ocean. However, the variability of particulate organic carbon (POC) and its response to climate forcing remain poorly understood. Using multi-source satellite observations and reanalysis data from 1998–2025, we investigated the spatial, seasonal, interannual, and decadal variability of POC, chlorophyll-a (Chl-a), and POC:Chl-a in the Agulhas Current system. Our results show that Chl-a and POC concentrations are consistently higher in the Agulhas Retroflection than in the Return Current region, reflecting enhanced mesoscale activity and nutrient supply. Seasonally, Chl-a and POC peaked during austral spring–summer and declined during autumn–winter, whereas POC:Chl-a exhibited an opposite cycle driven by variations in light availability and mixed-layer depth. At interannual timescales, ENSO exerted a pronounced influence on POC variability. El Niño events increased Chl-a and POC by up to 0.3 mg m⁻³ and 30 mg m⁻³, respectively, while reducing POC:Chl-a by up to 60 g g⁻¹ through enhanced eddy activity and improved light conditions; the opposing anomalies occurred during La Niña events. Positive Southern Annular Mode (SAM), phases increased Chl-a and POC in the Return Current region by strengthening vertical mixing and nutrient entrainment. On multi-decadal timescales, contrasting regional trends resulted in a persistent increase in the POC:Chl—a ratio across both regions, suggesting a structural shift in the particulate carbon pool and an increasing decoupling between particulate organic carbon and phytoplankton biomass. These results highlight the combined roles of stratification, mesoscale dynamics, and climate modes in regulating regional carbon cycling and carbon-sink variability. Full article

In recent years, with the significant decline in fine particulate matter (PM_2.5) concentrations, ozone (O₃) has emerged as a major composite air pollutant during the warm season in China, attracting increasing attention due to its associated health burden and economic costs. This study focuses on Tianjin, using ozone monitoring data from 2017 to 2023 combined with health statistics to assess the health impacts and economic losses attributable to ozone pollution. First, ozone exposure indicators and compliance criteria were constructed based on national air quality standards, and the interannual variation and spatial differences of O₃ levels were analyzed at both citywide and district scales. Second, multiple machine learning classification models, including logistic regression, decision tree, k-nearest neighbors, and gradient boosting, were developed using ozone and meteorological variables to predict the occurrence risks of five diseases: cardiovascular diseases, respiratory diseases, hand-foot-and-mouth disease (HFMD), influenza, and dengue fever. Finally, excess cases were estimated using health impact functions, and the associated economic losses were quantified by combining the value of a statistical life (VSL) with cost-of-illness and willingness-to-pay (WTP) approaches. The results showed that the annual evaluation value of ozone in Tianjin, defined as the 90th percentile of the daily maximum 8 h average O₃ concentration, exhibited a pattern of initially increasing, then decreasing, and subsequently rebounding. It peaked at 201 µg/m³ in 2018, declined to a minimum of 164 µg/m³ in 2021, and rebounded to 188 µg/m³ in 2023. Machine-learning results indicated that the logistic regression model showed relatively stable overall performance across predictions of different diseases, while the gradient boosting tree model also achieved high accuracy in predicting certain infectious diseases. Overall, ozone pollution exhibits significant heterogeneous effects across different disease types, and the associated health-related economic losses show stage-wise fluctuations in response to pollution levels. Based on these findings, it is recommended to implement refined control measures during periods of high ozone exceedance and in key regions, while strengthening protection for vulnerable populations such as the elderly, children, and patients with respiratory diseases, in order to achieve synergistic improvements in air quality management and public health outcomes. Full article

Monitoring species richness patterns across large spatial scales is essential for addressing the global biodiversity crisis. Dynamic Habitat Indices (DHIs), derived from satellite-based productivity data, have proven valuable for predicting species distributions. The original DHI framework comprises three complementary sub-indices, each corresponding to a key ecological hypothesis linking productivity and biodiversity: annual cumulative productivity (DHI Cum; available energy hypothesis), annual minimum productivity (DHI Min; environmental stress hypothesis), and the coefficient of variation in productivity (DHI CV; environmental stability hypothesis). However, current DHI formulations primarily focus on intra-annual vegetation productivity dynamics, thereby overlooking the ecological significance of inter-annual productivity variability. To address this limitation, we propose an extended DHI suite that integrates both seasonal (intra-annual) and long-term (inter-annual) productivity metrics. Using a random forest regression approach, we demonstrate that incorporating this extended DHI suite significantly improves predictions of global vertebrate species richness (cross-validated R² = 0.89, RMSE = 68.20) compared to using seasonal metrics alone (R² = 0.86). Notably, inter-annual productivity variation emerged as the most influential predictor, strongly supporting the environmental stability hypothesis. This was followed by importance in seasonal minimum productivity (environmental stress) and cumulative productivity (available energy). Our findings reveal the critical, complementary roles of seasonal and inter-annual productivity dynamics in shaping global faunal species richness patterns. This enhanced framework provides a robust scalable tool for assessing species richness distributions and informing conservation strategies amid accelerating climate shifts and anthropogenic pressures. Full article

A total of 3930 blackfin flounder (Glyptocephalus stelleri) individuals were collected continuously on a monthly basis from the East Sea of Korea in 2024 (n = 1800) and 2025 (n = 2130). The total length ranged from 10.6 to 44.0 cm in 2024 and from 11.9 to 49.7 cm in 2025. The major prey items differed between the years. In 2024, polychaetes (75.3%) and amphipods (12.2%) were the dominant prey items, whereas in 2025, euphausiids (33.1%), polychaetes (33.7%), and fish (17.5%) were the most important prey groups, indicating a clear interannual variation in diet composition. PERMANOVA revealed that diet composition varied significantly with year, season, and size class (p < 0.05), with a significant interaction between the year and season. These patterns were consistently supported by the CAP ordination, which showed a clear separation of samples along the seasonal gradient on the CAP1 axis, with additional variations associated with the year and size class observed within the respective seasonal groupings. Ultimately, these results suggest that G. stelleri functions as an opportunistic feeder that is capable of shifting its diet in response to environmental fluctuations. This study aims to provide scientific data for efficient fishery resource management and ecosystem-based assessments in response to future climate change. Full article

With the continuous reductions in anthropogenic NOx emissions and persistent surface O₃ pollution in China, soil NO emissions have become an increasingly important component of the regional NOx budget. In this study, an updated Berkeley–Dalhousie Soil NO Parameterization model driven by MERRA-2 reanalysis data was used to develop a 20-year soil NO emission inventory for China from 2001 to 2020. Multiple sensitivity scenarios were designed to quantify the relative contributions of nitrogen fertilizer application, meteorological variations, land use changes, and canopy factors on the interannual variations in soil NO emissions. The results showed that soil NO emissions exhibited an overall pattern of initial increase followed by fluctuating decline, with an average annual emission of 0.92 ± 0.05 Tg N year⁻¹ and a peak of 0.98 Tg N year⁻¹ in 2014. Summer was the dominant emission season, accounting for 57.7–61.9% of annual emissions. Spatially, emissions were concentrated in agriculturally intensive regions, particularly East China and Central China. With the decline in anthropogenic NOx emissions, the relative contribution of soil NO to total NOx emissions showed a recovery after 2012, indicating its increasing importance in future NOx budget assessments. Driver attribution analysis showed that nitrogen fertilizer application determined the long-term emission potential, whereas meteorological conditions regulated interannual and seasonal variability. These findings highlight the need to incorporate soil NO emissions into sustainable nitrogen management and ozone-related air quality assessments. Full article

Understanding the spatiotemporal evolution of runoff and its driving mechanisms is of great significance for water resources development, utilization, and sustainable management in mid- to high-latitude river basins under climate change. However, runoff variability is jointly influenced by multiple meteorological factors, and a comprehensive understanding of its multi-scale response characteristics and the relative contributions of different drivers remains limited. In this study, runoff data from three hydrological stations in the Songhua River Basin during 1980–2022 were analyzed. A set of statistical and time-series methods, including the Mann–Kendall test, Pettitt change-point test, Hurst exponent, wavelet analysis, and wavelet coherence, was applied, and a random forest model was used to quantify the influence of key climatic factors such as precipitation, air temperature, and evapotranspiration. The results show that air temperature exhibits significant increasing trends in all four seasons, with the strongest warming occurring in spring (Sen’s slope ≈ 0.06 °C a⁻¹). Precipitation displays pronounced spatial heterogeneity and interannual variability, while evapotranspiration shows an overall increasing trend. Both runoff and major meteorological variables exhibit significant spatial heterogeneity across the basin. Hydro-meteorological variables also show distinct periodic variations among seasons, with temperature, precipitation, and evapotranspiration exhibiting stronger seasonal fluctuations during summer. Wavelet coherence analysis indicates that short-term runoff variability is mainly driven by temperature and precipitation. Temperature exhibits significant coherence with runoff across multiple time scales ranging from approximately 2 to 20 years, whereas precipitation shows stronger coherence at medium- to long-term scales (approximately 10–35 years), with evident seasonal differences. Random forest results indicate that evapotranspiration is the most important contributor to runoff variability at all three stations, accounting for 33.5%, 28.6%, and 26.2% of the total importance at Jiamusi, Fuyu, and Jiangqiao stations, respectively. Temperature and sunshine duration rank second, while precipitation and relative humidity contribute comparatively less. These findings indicate that evapotranspiration plays a key regulatory role in long-term water balance. In addition, runoff exhibits multi-scale variability and a transition from gradual changes to stage-like abrupt shifts. The findings provide a scientific basis for water resources management, flood mitigation, and climate change adaptation in the Songhua River Basin. Full article

Long-term ichthyoplankton time series provide an effective framework for understanding how marine communities respond to environmental variability across temporal scales. We analyzed larval fish assemblage dynamics in the central Mexican Pacific under contrasting seasonal hydrographic conditions and ENSO phases using multivariate analyses, indicator species analysis, clustering, and generalized additive models. Environmental variability exhibited a hierarchical structure, with recurrent seasonal changes in sea surface temperature (SST) and coastal upwelling intensity (CUI), whereas the Oceanic Niño Index (ONI) varied mainly at the interannual scale. Significant differences in assemblage composition were detected among ENSO–seasonality regimes. Distance-based redundancy analysis showed that the primary compositional gradient was associated with seasonal hydrography, while secondary variation reflected ENSO-related interannual shifts. Species responses were expressed primarily through shifts in relative dominance rather than wholesale species replacement, indicating that assemblage reorganization was largely driven by changes in the relative contribution of recurrent taxa. This pattern highlights the role of seasonal hydrography as the primary environmental filter structuring the assemblage, whereas ENSO variability acts mainly as a secondary modulator of species dominance and community trajectories. Consequently, interannual climate anomalies influenced the relative importance of species without substantially redefining the underlying species pool. These findings improve the understanding of plankton community responses to climate variability in the tropical eastern Pacific. Full article

To identify upland cotton varieties with consistently high yields and stable performance across variable growing seasons in Xinjiang, we evaluated yield data for 11 varieties over 4 consecutive years (2022–2025). Among the tested varieties, 02 achieved the highest average yield (10.85 kg per plot). Variety ZMBH1939 showed the most stable yield across years (coefficient of variation = 0.1557). Analysis of variance showed that variety, year, and their interaction significantly affected yield (p < 0.01 for all). Further evaluation using two complementary multi-environment trial models (AMMI and GGE) revealed consistent findings: 02 and FC190 were high-yielding but moderately stable; W21 and TH02 showed moderate yield with good stability; and XLM108 combined high yield potential with excellent stability. The control variety Z49 (CK) exhibited good stability but only moderate yield. Among the four trial years, 2023 was the most representative and discriminatory environment, making it ideal for screening superior varieties. Exploratory analysis of climatic covariates suggested that accumulated temperature (≥10 °C) may be associated with interannual yield variation (R² = 0.464), and low precipitation was linked to stronger environmental discrimination. However, given the limited number of environments (n = 4), these findings are preliminary and hypothesis-generating rather than confirmatory. This study provides a framework for understanding climate-driven yield variation in regional cotton trials and identifies promising germplasm (notably XLM108 and 02) for further breeding and promotion. Validation in multi-location or longer-term trials is required before drawing definitive conclusions. Full article