Search Results (153)

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

Modern agriculture must balance productivity with sustainability. In this context, unmanned aerial vehicles (UAVs) offer flexible, cost-effective tools for crop and soil monitoring in precision agriculture. This study aimed to evaluate the potential of UAV-borne RGB imagery, combined with vegetation indices and machine learning, to estimate surface soil properties and crop physiological traits in peanut (Arachis hypogaea) cultivation. A factorial field experiment with four varieties, two planting densities, and two tillage systems was monitored using high-resolution RGB orthomosaics acquired at key phenological stages. From these images, 17 RGB-based indices were computed and related to soil variables and crop traits using Spearman correlation and two regression algorithms: Random Forest (RF) and k-Nearest Neighbors (KNN). RF models outperformed KNN, with the Red Chromatic Coordinate (RCC) index achieving an R² of 0.87 for predicting soil organic matter content. Indices such as visible NDVI and the Green Vegetation Index also provided robust estimates of canopy condition and leaf chlorophyll. Overall, the results demonstrate that UAV RGB imagery, processed through simple vegetation indices and RF models, constitutes an effective, low-cost approach for monitoring key agronomic parameters in peanut farming. Full article

Due to their aesthetic qualities and versatile applications, medicinal and aromatic plants are an important component of landscape systems. The diversity of color, shape, and texture observed in the vegetative and reproductive organs of these plants contributes to visual composition, while their medicinal and aromatic properties enhance their ecological and socio-cultural significance. However, many taxa are underrepresented in landscape planning applications. This study examined the diversity of medicinal and aromatic plant taxa identified at the Iğdır University Şehit Bülent Yurtseven Campus in Iğdır Province, Turkey, using a descriptive approach. Plant taxa were evaluated based on their families, life forms, leaf characteristics, flowering periods, and medicinal and aromatic properties. Multivariate analyses were conducted to examine phenological similarities among the taxa. A total of 98 plant taxa were identified; 66 taxa possess only medicinal properties, one taxon possesses only aromatic properties, and 31 taxa possess both. These findings reveal that the campus is home to a wide variety of medicinal and aromatic plant taxa, with characteristics relevant to planting layout and species selection. Consequently, this study provides a descriptive foundation for further research on how such taxa can be incorporated into campus planting designs and green space planning. Full article

Olive groves in Mediterranean regions are being increasingly exposed to drought and heat extremes, intensifying the interannual yield variability. This study presents an integrated smart-farming framework that links soil context, climate forcing and satellite-observed canopy dynamics to enhance the interpretability and transferability of yield indicators at the parcel scale in southern Spain. Using SoilGrids root-zone properties and the Sentinel-2 time series of the normalized difference vegetation index (NDVI), we first classified parcels into three edaphic clusters. The canopy development was then expressed in thermal time using growing degree days (GDD), enabling phenology-aligned comparisons across campaigns. Two robust patterns emerged: (i) the cumulative NDVI up to 520 GDD showed a consistent negative association with both the biomass and the oil yield, suggesting an early-season vegetation trade-off and carry-over effects typical of perennial systems, and (ii) the rainfall accumulated during a thermally defined window (120–480 GDD) strongly estimated the yield in the subsequent year ($R^2=0.83$–0.97 across soil clusters). By anchoring both vegetation and precipitation indicators to physiologically meaningful thermal milestones, the proposed framework avoids arbitrary calendar windows and enhances the interpretability, cross-year comparability, and scalability. Under projected increases in drought frequency and heat extremes, such hydro-thermal scaling approaches offer a robust basis for early yield forecasting, cooperative-level production planning, and adaptive management in Mediterranean olive systems. Full article

Accurate and early prediction of crop yield at the sub-field scale is essential for precision-agriculture and food-system planning. This study evaluates a phenology-based machine learning framework for winter wheat yield prediction using Sentinel-2 satellite imagery, climate reanalysis data, and field-level yield data. Phenological metrics derived from the normalised difference vegetation index (NDVI), the normalised difference water index (NDWI), and the normalised difference red-edge index (NDRE) were combined with accumulated seasonal rainfall and seasonal potential evapotranspiration, and multiple modelling strategies were assessed using a leave-one-field-out cross-validation (LOFO CV) scheme to ensure spatial generalisation. Among the evaluated models, the Random Forest (RF) algorithm achieved the highest overall performance, explaining up to 73% of the yield variability with a root mean square error (RMSE) of 0.88 t ha⁻¹ at optimal prediction timing (day of year 160–175). Integrating phenological and climatic covariates consistently improved prediction accuracy compared to models based only on phenological variables, while the inclusion of soil properties provided limited additional benefit at the examined spatial scale. Phenological metrics based on red-edge data, particularly the maximum NDRE, were the most influential predictors, highlighting the added value of red-edge spectral information beyond traditional red–near-infrared indices. Uncertainty analysis revealed spatially heterogeneous prediction uncertainty, particularly near field boundaries and in areas of complex spatial patterns. Overall, the proposed framework enables robust, early, and interpretable yield prediction at the sub-field scale, supporting uncertainty-aware decision-making in precision agriculture and offering a scalable foundation for regional crop monitoring. Full article

Addressing the intertwined challenges of food security and climate vulnerability requires robust and regionally tailored strategies for staple crops such as common beans. Although adjusting sowing dates is a key adaptive practice, spatio-temporal climate variability complicates the identification of optimal planting windows. This study integrates crop modeling with Functional Data Analysis (FDA) to quantify sowing-date-dependent yield losses for rainfed common beans across Central-Southern Brazil. The CSM-CROPGRO-Dry Bean model, driven by long-term climate data (1980–2016), soil properties, and management practices, was used to simulate yields for the BRS Estilo cultivar. FDA was subsequently applied to cluster yield-loss curves across municipalities and growing seasons, generating representative regional risk profiles. The results reveal clear spatial patterns. During the wet season, earlier sowing minimizes losses in Goiás, Minas Gerais, and western Paraná, whereas later sowing is beneficial in São Paulo, Santa Catarina, and eastern Paraná. In the dry season, earlier sowing consistently reduces losses across most regions. These patterns are primarily driven by water deficits and suboptimal temperatures during critical phenological phases. The resulting spatio-temporal sowing calendar provides an evidence-based decision-support tool to help farmers mitigate climatic risks. Moreover, it offers a scientific foundation for policymakers to refine sustainable management practices, improve crop insurance design, and enhance agricultural resilience and productivity under increasing climate uncertainty. Full article

Effective management zone delineation is key to implementing site-specific strategies that address spatiotemporal heterogeneity in agriculture. Although time-series remote sensing offers a dynamic perspective, most current methods lack the framework to integrate it with soil properties, thereby hindering accurate characterization of crop growth variability. This study bridges the gap by developing a spatiotemporal framework that synthesizes remote sensing-derived phenology and soil attributes for daylily management zoning. Through a sequential approach—phenological metric extraction, SNIC-based segmentation, and STSF classification—we produce refined phenological time-series stacks. These outputs are designed to elucidate the drivers of field heterogeneity and directly inform precision management strategies. Compared to pixel-based and SNIC-based random forest, the STSF–SNIC framework increased spatial overlap rates by 5.4–8.0% (reaching 88.6%), despite comparable overall accuracy and kappa coefficients (OA/kappa: 92–94%). Geographical detector analysis identified village boundaries, soil type, total nitrogen, and organic carbon as key drivers of spatial patterns. A spatial generalized fuzzy c-means model, incorporating crop growth dynamics and soil gradients, reduced management zone fragmentation by 27.8% compared to conventional methods, with spatial autocorrelation analysis confirming enhanced spatial consistency (Moran’s I = 0.600 vs. 0.433, p < 0.001). In conclusion, by integrating time-series remote sensing phenology with soil attribute analysis within a spatially constrained clustering scheme, this study (1) provides a novel method for delineating coherent management zones, (2) reveals key drivers of crop growth heterogeneity, and (3) demonstrates a transferable pathway for translating satellite data into precision management actions. It thereby exemplifies the value of applied remote sensing in addressing practical challenges in sustainable agriculture. Full article

Microbial stoichiometry serves as a fundamental indicator of nutrient limitations in microbial communities. However, the dynamic effects of Pugionium Gaertn. growth on soil microbial C:N:P stoichiometric ratios and their primary driving factors in native desert ecosystems remain poorly understood. This study aimed to clarify the stage-dependent regulation of microbial C:N:P stoichiometry by Pugionium Gaertn. in native desert ecosystems. This study examined representative Pugionium Gaertn. (P. cornutum and P. dolabratum) in northwestern China’s desert regions, based on investigations conducted during 2022–2023, conducting systematic analysis of variations in rhizosphere soil microbial biomass C, N, and P levels, C:N:P stoichiometric ratios, fungal and bacterial diversity, soil physicochemical properties, and extracellular enzyme activities (EEAs) across different phenological stages. Results demonstrated that Pugionium Gaertn. growth significantly enhanced microbial biomass C, N, and P accumulation during vigorous growth stages. Simultaneously, stoichiometric ratios (C:N, C:P, N:P) exhibited periodic fluctuations, with P limitation characteristics becoming substantially intensified during the reproductive stage. Total soil nitrogen, total phosphorus, and EEAs significantly regulated microbial C:N:P stoichiometric ratios through their effects on bacterial diversity. In P. dolabratum, distinct response pathways were observed between fungi and bacteria to P limitation, indicating species-specific regulatory mechanisms. These findings provide novel insights into the relationship between Pugionium Gaertn. and soil elemental stoichiometry, as well as its influence on elemental dynamic balance at microbial and community levels. Furthermore, the results support ecological adaptation strategies of Pugionium Gaertn. communities in native habitats, offering scientific evidence for vegetation restoration and soil improvement in desert regions. Full article

Complex interactions in soil carbon and nitrogen (C-N) synchronisation in tropical perennial orchards are highly responsive to fertiliser chemistry. However, the intensity and stage-specific dynamics of these interactions are not well quantified. Six nitrogen regimes, namely, urea (URT), ammonium (AMT), nitrate (NT), slow-release fertiliser (SRT), bio-organic fertiliser (BFT), and an unfertilised control, were assessed at the vegetative, flowering, fruit-set, and maturity stages of durian cultivated on highly weathered tropical soils. A two-way ANOVA indicated high to very high treatment × phenology interactions for almost all soil properties (p < 0.001), indicating that nutrient responses were highly stage-dependent. The highest soil organic carbon (SOC) and cation exchange capacity (CEC) values were consistently obtained with the BFT, which was often associated with significant differences compared with synthetic treatments. In contrast, the SRT showed the most consistent nutrient release behaviour, especially in flowering. On the other hand, soil pH did not differ significantly among the treatments during the vegetative and maturity stages. A significant decrease in pH was observed for the URT and NT treatments during the flowering stage, indicating temporary acidification at this stage and steep increases in nitrate nitrogen (NO₃^—N), indicating strong nitrification and attenuated carbon (C) stabilisation. Leaf nutrient responses were increased in phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) by 23% in response to the SRT and BFT. The NT and URT tended to enhance leaf nitrogen (N) primarily, and PCA (59–69% variance explained) clearly displayed clustering of the fertiliser effects, with the maximum difference at flowering, the peak period of nutrient demand in the crop. In general, fertiliser chemistry and phenophase jointly controlled the C-N partitioning, soil chemical paths, and nutrient yield correlations. The BFT and SRT showed the greatest significant gains in soil fertility and nutrient retention, making them the best high-performance alternatives in sustainable durian production in tropical systems. Full article

Intensive greenhouse management profoundly alters soil biogeochemical processes and biotic interactions, distinguishing greenhouse soils from open-field systems. Understanding the drivers of soil fauna assembly is essential for sustaining soil health and productivity. In this study, we examined nematode community drivers in greenhouse melon systems under 2- and 10-year rotations using environmental DNA sequencing. Plant phenology, more than rotation, shaped nematode communities, particularly omnivore predators and bacterivores. This driver was mirrored by a shift in nematode faunal indices from an enriched, bacterial-dominated state at seedling stages to a structured state at maturity. LDA Effect Size and random forest identified key genera (Prismatolaimus, Acrobeloides, and Ceramonema), demonstrating multidimensional drivers of community assembly. Redundancy analysis showed soil organic matter (SOM) and acid phosphatase as major drivers. Mantel tests indicated that the microbial biomass carbon and nitrogen ratio (MBC/MBN) consistently explained community variation (relative abundance: r = 0.229; functional diversity: r = 0.321). Structural equation modeling linked available phosphorus to microbial carbon cycling via cumulative carbon mineralization (CCM, 0.41) and MBC (0.40). SOM increased MBN (0.62) but suppressed Chao1 (−0.76). MBN had the strongest positive effect on Pielou_e (0.49). pH negatively affected functional diversity (−0.33), while nitrate nitrogen (0.35) and CCM (0.32) had positive effects. Our results indicate that MBC and MBN act as microbial bridges linking soil properties to nematode diversity, providing a mechanistic basis for optimizing greenhouse soil management and ecosystem functioning. Full article

In Europe, Salvia officinalis L. is the most widely cultivated species of the genus Salvia, valued for its medicinal properties and essential oil production. However, in Spain, the predominant wild species is S. lavandulifolia Vahl., which exhibits notable morphological diversity. Cultivating these species presents specific challenges: S. lavandulifolia typically displays a creeping habit that hinders mechanical harvesting, while S. officinalis contains neurotoxic thujones in its essential oil, raising safety concerns. Therefore, developing new sage cultivars that combine improved agronomic performance, easier harvesting, and a safe, high-quality essential oil composition is of great practical interest for the sustainable production of sage. This study investigates the recently described natural hybrid Salvia × accidentalis nothosubsp. albaladejitoi (S. lavandulifolia subsp. lavandulifolia × S. officinalis) through a comprehensive multiparametric evaluation, including morphological, phenological, and biochemical analyses. The hybrid exhibited greater biomass, likely influenced by S. officinalis, which could facilitate mechanical harvesting. The chemical profile (GC and HPLC) revealed intermediate compositions of the essential oil and extract, characterized by lower concentrations of thujone and camphor and higher levels of bioactive pinenes. Its balanced phenolic profile and enhanced antioxidant capacity also suggest potential functional applications. Overall, S. × accidentalis nothosubsp. albaladejitoi demonstrates a promising combination of agronomic and biochemical traits, supporting its potential as a new cultivar for the sustainable cultivation of sage and the production of high-quality, safe and functionally valuable sage-derived products. Full article

The projected climate change in Slovakia is expected to have a significant impact on temperature and moisture conditions in agricultural production, as well as on phenological patterns and soil properties. These alterations have the potential to diminish crop yields in regions experiencing summer heat, augment soil evaporation, and elevate the probability of drought. The objective of this study was to evaluate and revise the spatial extent of vegetation zones and agricultural land. A detailed analysis of the past 30 years revealed that the growing season has become both earlier in the year and later in the year in terms of its onset and cessation. Projections indicate that, by 2091–2100, the great growing season (GGS) will be 25–30 days longer and the main growing season (MGS) 20 days longer than at present. The results indicate that the extended growing seasons will encompass larger areas and gradually shift to higher altitudes. At present, the 220–240-day category of the GGS spatial domain is dominant (1.7–2.3 million hectares), while durations of the GGS exceeding 260 days, which were absent in the 1971–1980 period, are expected to increase the area of the growing season by approximately 55,000 hectares by 2100. For the MGS, the 160–190-day category remains prevalent (approximately 2.5 million hectares), with only moderate future increases of up to 220 days being expected. It is anticipated that extended durations will remain constrained, encompassing less than 50,000 hectares. Full article

Potatoes are an economically important crop in Virginia, USA, where growers must balance planting dates, nitrogen (N) management, and variable crop prices. Early planting exposes crops to low temperatures that limit growth, whereas late planting increases pest pressure and nutrient inefficiency. This study evaluated the effects of planting dates, N rates, and application timing on potato growth, yield, and pest incidence. We also assessed whether soil physicochemical properties could predict the presence of wireworms and plant-parasitic nematodes (PPNs) using complementary on-farm samples collected across Eastern Virginia between March and July 2023. Three planting dates (early-March, late-March, and early-April) were combined with five N rates (0, 146, 180, 213, and 247 kg N·ha⁻¹) under early- and late-application regimes. We collected data on plant emergence, flowering time, soil nitrate, biomass, tuber yield, pest damage, and UAS-derived metrics. Results showed that late-March planting with 180 kg N·ha⁻¹ achieved the highest gross profit while maintaining competitive yields (25.06 Mg·ha⁻¹), representing 24% and 6% improvements over traditional practices, respectively. Early-April planting produced the largest tubers, with a mean tuber weight 19% higher than the other planting dates. The Normalized Difference Red Edge Index (NDRE) was strongly correlated with N content in plant tissue (R² = 0.81; r ≈ 0.90), and UAS-derived plant area accurately predicted tuber yield 4–6 weeks before harvest (R² = 0.75). Wireworm damage was significantly higher in early-March plantings due to delayed insecticide application, while soil nitrate concentration and percent H saturation were identified as key predictors of wireworm presence. Although less effectively modeled due to limited sample size, PPN occurrence was influenced by potassium saturation and soil pH. Aligning planting dates and nitrogen applications with crop phenology, using growing degree days (GDD), enhanced nitrogen management, and yield prediction. Full article

Climate change represents a critical challenge for viticulture worldwide, primarily through increased heat stress, more frequent and severe drought periods, and unseasonal rainfall events. There is increasing evidence of its negative effects on both thermal regimes—potentially leading to accelerated phenology and unbalanced sugar-to-acid ratios—and hydric regimes—causing water stress that impacts berry development and final yield. The use of plastic covering in vineyards is a widespread technique, particularly in regions with high climatic variability such as the Mediterranean Basin (e.g., Southern Italy, Spain, Greece), aimed at protecting both vegetation and grapes from external factors such as hail, heavy rainfall, wind, and extreme solar radiation, which can cause physical damage, promote fungal diseases, and lead to berry sunburn. This study explores the impact of six distinct commercial plastic films, with varying optical properties, on the retrieval and accuracy of vegetation indices derived from Sentinel-2 imagery in a mid-season table grape vineyard (Autumn Crisp^®) in Southern Italy during the 2024 growing season. Laboratory spectroradiometric analyses were conducted to measure film-specific transmittance and reflectance factors from 200 to 1500 nm, enabling the development of a first-order linear spectral correction model applied to Sentinel-2 imagery. Vegetation indices (NDVI, CVI, GNDVI, LWCI) were corrected for plastic interference and analysed through univariate statistics and Principal Component Analysis. Results showed that after applying the spectral correction model, film T2 displayed the higher NDVI value (0.73). Films T3 and T4—characterised by high visible light transmittance (>39%) and low reflectance (<11% in the Red/NIR)—resulted in lower vine vigour and photosynthetic activity, with mean corrected NDVI values equal to 0.70, though still significantly higher than those of films T1 (0.65) and T5 (0.67). Films T6 and T1 were associated with greater water conservation, as indicated by the highest mean LWCI values (T6: 0.59; T1: 0.52), but lower chlorophyll-related signals, evidenced by the lowest mean CVI values (T6: 1.31; T1: 1.74) and GNDVI values (T6: 0.46; T1: 0.48). Among the corrected indices, NDVI demonstrated strong positive correlations with yield (r = 0.900) and total soluble solids per vine (TSS*vine, in kg), a key quality parameter representing the total sugar yield (r = 0.883), supporting its suitability as an index for vine productivity and fruit quality. The proposed correction method significantly improves the reliability of remote sensing in covered vineyards, as demonstrated by the strong correlations between corrected NDVI and yield (R² = 0.810) and sugar content (R² = 0.779), relationships that were not analysable with the uncorrected data; may guide film selection—opting for high-transmittance films (e.g., T2, T3) for yield or water-conserving films (e.g., T6) for stress mitigation—and irrigation strategies, such as using the corrected LWCI for precision scheduling. Future efforts should include angular effects and ground-truth validation to enhance correction accuracy and operational relevance. Full article

In the context of natural beverages used for human nutrition, our study explored the potential of Myrcianthes hallii leaves (rich in bioactive compounds) as a raw material for the production of non-traditional craft beer. We hypothesized that the phenological stage affects essential oil yield and bioactivity, which in turn influences the functional properties of fortified beer. In our case, M. hallii leaves collected during the flowering stage yielded the highest amount of essential oil (0.5 v/m/%) and exhibited the greatest concentrations of total phenolics (7.7149 ± 0.02143 mg GAE/mL) and flavonoids (1.6531 ± 0.03355 mg QE/mL), correlating with increased antioxidant capacity. These findings suggest this stage as the most suitable period for harvesting M. hallii leaves for nutraceutical and pharmaceutical applications. This non-traditional beer demonstrated notable antioxidant activity, and sensory analysis revealed high acceptance regarding aroma, taste, and color, supporting its potential as a functional beverage. Full article