Search Results (189)

Long-term fertilization regulates soil microbial communities and is essential for black soil health and sustainable productivity, yet its key drivers remain unclear. Using a 39-year field experiment, we evaluated the effects of four fertilization regimes: no fertilizer (CK), chemical fertilizer (NPK), organic fertilizer (M), and combined organic-inorganic fertilizer (MNPK). Soil properties and bacterial communities were analyzed using Illumina MiSeq sequencing, quantitative real-time PCR (qRT-PCR), and multivariate analyses. Proteobacteria, Actinobacteriota, Acidobacteriota, Chloroflexi, and Gemmatimonadota dominated (>80% of the community), and all treatments significantly altered their relative abundances. Compared with CK, NPK reduced soil pH by 8.3% and bacterial abundance by 29.7%, increased soil organic matter (SOM) by 22.9%, and decreased community evenness. MNPK reduced pH by only 2.0%, increased SOM by 53.8% and bacterial abundance by 38.9%, and improved community evenness, mitigating acidification while maintaining high diversity. M increased pH by 2.3%, SOM by 73.3%, and bacterial abundance by 71.8%. Soil pH, available phosphorus, and SOM were the main drivers of community structure. Overall, MNPK showed the strongest synergistic effects on soil fertility and microbial stability, making it an optimal strategy for sustainable black soil management. Full article

Groundwater nitrate contamination, coupled with long-term overexploitation and intensive anthropogenic perturbations, has become a critical environmental challenge in the northwestern North China Plain, underscoring the urgent need to elucidate groundwater hydrochemical characteristics and their genetic mechanisms. Taking the upper section of the Yongding River alluvial–proluvial fan as the study area, this research aims to quantitatively decipher the hydrochemical characteristic and genetic mechanism of high-nitrate groundwater, identify the sources of nitrate contamination, and assess the associated human health risks. By leveraging over a decade of continuous hydrochemical monitoring data, an integrated analytical approach is adopted, including hydrochemical ionic ratio analysis, Positive Matrix Factorization, and Human Health Risk Assessment. The results indicate that the groundwater is characterized by HCO₃-Ca. The pH values range from 7.2 to 8.2 while the total dissolved solids concentrations vary between 695 mg/L and 949 mg/L. Ionic ratio analysis demonstrates that water–rock interaction is the dominant controlling process, involving silicate hydrolysis, dissolution of carbonates, gypsum dissolution, and cation exchange. The Positive Matrix Factorization model quantitatively identifies four key factors controlling the hydrochemical characteristics of groundwater. Factor 1 is dominated by NO₃⁻ (76.67%) and associated with exogenous nitrate inputs from nitrogen fertilizer application. Factor 2 is dominated by Na⁺ (72.26%) and Mg²⁺ (81.67%), deriving from silicate weathering and dolomite dissolution. Factor 3 is governed by pH (59.62%) and K⁺ (71.65%), with its driving mechanism being the weathering and dissolution of potassium-bearing silicate minerals. Factor 4 is dominated by SO₄²⁻ (50.12%) and constitutes a mixed source associated with sulfur-containing fertilizer application and livestock breeding. Groundwater NO₃⁻ concentrations range from 4.2 mg/L to 23.3 mg/L, with 69% of dry-season and 77% of wet-season samples exceeding the 10 mg/L threshold, primarily originating from manure and domestic wastewater. HHRA results show that nitrate poses significant non-carcinogenic health risks, with the highest risk observed in children (100% of samples at high risk), followed by adult females (92% at high risk) and adult males (77~92% at high risk). This study provides quantitative insights into the genetic mechanisms of groundwater nitrate contamination and offers a scientific basis for groundwater quality management and health risk mitigation in the NCP and other similar agricultural regions worldwide. Full article

This study demonstrates the application of Laser-Induced Breakdown Spectroscopy (LIBS) for the elemental analysis of agricultural soils in South Punjab, Pakistan. Soil degradation due to intensive farming, imbalanced fertilizer use, and declining organic matter has reduced crop productivity in the region. To address this, rapid and accurate soil diagnostics are essential. LIBS, coupled with Calibration-Free analysis (CF-LIBS), was employed to quantitatively determine the concentrations of major and trace elements—including calcium, silicon, iron, aluminum, magnesium, titanium, potassium, sodium, lithium, and barium—without requiring chemical standards. Plasma characterization was performed using the Boltzmann plot method, yielding temperatures between 7750 and 9000 K, and electron number densities were derived from Stark-broadened spectral profiles. The results reveal significant spatial variability in elemental composition, reflecting differences in land use and irrigation sources. This work confirms LIBS as a versatile, efficient, and reliable tool for soil health assessment, offering a practical solution for monitoring soil nutrients and supporting sustainable agricultural management in resource-limited settings. Full article

Soil–Forage–Livestock systems (SFL-systems) integration is fundamental for sustainable land management in arid lands, where conventional crop production is often unfeasible. Aridisols dominate dryland agroecosystems and their edaphic constraints, together with climatic limitations, constitute a major bottleneck for fertility and productivity in key arid regions worldwide. This narrative review provides a taxonomic and edaphic framework to guide sustainable SFL-systems and integrates current approaches and technological applications for forage production in arid environments, focusing on an edaphic-digital scheme that combines organic and inorganic soil amendments with AI-based decision support to improve Aridisols productivity and resilience. Searches of the literature (ScienceDirect, EBSCOhost, Clarivate Web of Science; English, 2021–2025) screened 309 records and selected 169 references; seminal older works were consulted for context. Representative quantitative outcomes from the reviewed literature include SOC increases of ~15–30% after multi-year organic inputs; forage biomass gains of ~10–25% following amendments that correct sodicity; and water-productivity improvements up to ~30% with hydrogels or biochar. AI tools can improve soil diagnostics and amendment selection (diagnostic accuracy improvements of ~15–30% in recent studies) and generate predictive models of amendment–response that facilitate optimization of application rates and water use. The novel contribution of this review is the explicit linkage of SFL-systems and amendment-based soil restoration with AI-driven diagnostics and decision support, providing actionable metrics and research priorities to translate digital diagnostics into measurable forage gains in arid and semi-arid regions. Overall, the evidence suggests that targeted soil restoration, reinforced by AI-based support systems, is a feasible strategy to increase forage availability and ecosystem service provision in drylands. Full article

This study investigates the influence of agronomic management on the accumulation of bioactive compounds and the antioxidant capacity of Achillea millefolium L., a medicinal species of increasing relevance for pharmaceutical and nutraceutical applications. Different cultivation strategies were applied, including controlled drought stress, foliar fertilization, and inoculation with plant growth–promoting rhizobacteria (PGPR), in order to evaluate their impact on tissue-specific metabolic responses. The total antioxidant capacity (TAC) of flowers and roots was determined using FRAP, DPPH, and ABTS spectrophotometric assays, while metabolite profiling was performed by UHPLC–MS/MS analysis. Clear differences in antioxidant activity were observed among plant organs and cultivation treatments. Flower extracts showed intermediate antioxidant capacity, with FRAP values ranging from 55.86 to 66.55 mg TE g⁻¹ extract and the highest activity consistently recorded for treatment F_010 (addition of K, P fertilizers under water stress conditions and PGPR absence) across all assays. Root extracts exhibited substantially lower antioxidant values (FRAP 19.40–33.69 mg TE g⁻¹), although samples R_000 (no foliar fertilization, under water stress conditions and PGPR absence) and R_100 (no foliar fertilization, under water stress conditions and presence of PGPR) displayed comparatively higher radical scavenging activity. Metabolic profiling revealed a shared presence of caffeic acid derivatives and flavonoids, including mono- and di-caffeoylquinic acids and apigenin-related compounds, with marked quantitative differences among tissues. Overall, the results demonstrate that agronomic practices significantly influence the accumulation and distribution of antioxidant metabolites in A. millefolium L., highlighting the importance of cultivation strategies for optimizing the production of bioactive phytochemicals. Full article

Systematic comparisons of how plants with contrasting ecological strategies respond to extremely wide nutrient availability gradients remain limited. We investigated the physiological, photosynthetic, and growth adaptations of four plant species representing distinct ecological strategies: Triticum aestivum L. (C3 annual crop), Zea mays L. (C4 annual crop), Ipomoea aquatica Forssk. (C3 annual/perennial aquatic vegetable), and Canna glauca L. (C3 perennial wetland ornamental). Plants were grown hydroponically under nitrogen (N), phosphorus (P), and potassium (K) gradients ranging from 0% to 500% of standard Hoagland nutrient solution. The study results showed that all measured plant traits exhibited characteristic unimodal dose–response patterns. Optimal performance mostly occurred at 100–150% nutrient availability gradients. Severe inhibition or mortality occurred at extreme gradients. Simultaneously, different plant species displayed markedly varying response amplitudes and nutrient-specific sensitivities. Z. mays showed the highest nutrient use efficiency and broadest optimal ranges, particularly for N and K. C. glauca exhibited extraordinary N responsiveness (32-fold increase in photosynthetic rate) but narrow optimal ranges (e.g., 1.01 ± 0.15 μmol CO₂/(m²·s) at the 1% N treatment vs. 32.52 ± 3.33 μmol CO₂/(m²·s) at the 150% N treatment). I. aquatica showed pronounced P limitation with broad tolerance to supra-optimal N and K. T. aestivum displayed moderate responses with clear sensitivity to N limitation. Root–shoot ratios declined systematically with increasing nutrient availability across all plant species, following negative exponential functions. The results of data analyses revealed significant effects of N, P, and K availability on all the determined plant traits. Correlation analyses demonstrated tight coupling effects among physiological, photosynthetic, and growth traits, indicating integrated whole-plant responses to nutrient variations. These findings reveal that plant ecological strategy systematically modulates nutrient response patterns and provide a quantitative framework for species-specific nutrient management. This study provides a theoretical basis for precision fertilization of aquatic vegetables and wetland plants, and more broadly support species-specific nutrient management in controlled-environment agriculture. Full article

Date palm (Phoenix dactylifera L.) production relies on the availability of viable and physiologically active pollen during female flowering, making pollen storage an important strategy to overcome flowering asynchrony and ensure effective artificial pollination. In this study, we systematically reviewed and quantitatively synthesized the effects of pollen storage conditions on pollen physiological quality and reproductive performance in date palm. Following PRISMA guidelines, 22 experimental studies were identified in the qualitative synthesis, and comparable quantitative datasets were used for meta-analysis. Acetocarmine staining, the most commonly used method for assessing pollen stainability across studies, was selected as the standardized indicator of pollen stainability. Multilevel random-effects meta-regression models were applied to evaluate temporal deterioration patterns over storage periods of up to 24 months, while standardized forest plot meta-analyses were used to estimate pooled effects after 12 months of storage. The results revealed a strong temperature-dependent decline in pollen physiological quality. Acetocarmine stainability declined by −6.41, −3.10, −2.62, and −2.24% month⁻¹ under ambient, refrigerated, mild freezing, and moderate freezing conditions, respectively, whereas germination declined by −6.77, −1.86, −3.14, −1.09, and −1.05% month⁻¹ under ambient (23–25 °C), refrigerated (4–5 °C), mild freezing (−5 °C), moderate freezing (−20 °C), and deep freezing (−80 °C) conditions, respectively. After 12 months of storage, stainability, germination, and fruit set were significantly reduced relative to fresh pollen. In contrast, pollen storage had no significant effect on final fruit weight, suggesting that pollen deterioration primarily affects fertilization success rather than subsequent fruit development. The available evidence suggests that low-temperature storage represents the most effective strategy for preserving date palm pollen functionality. Refrigerated storage around 4 °C appears to provide a reliable and accessible option for short- to medium-term pollen preservation, whereas freezing conditions may be advantageous for longer storage periods when moisture control and thawing procedures are properly managed. Full article

Soil health monitoring is critical for the sustainable management of the black soil region, a key resource for global food security. However, traditional field surveys are constrained by high operational costs, limited spatial coverage, and low temporal frequency, making them inadequate for high-resolution and time-sensitive soil monitoring. The recently launched ZY1-02E satellite, equipped with an advanced hyperspectral imager, offers a new potential data source, yet its capability for quantitative soil modelling requires rigorous cross-sensor validation. This study conducts a cross-sensor evaluation of ZY1-02E and its predecessor, ZY1-02D, for mapping soil organic matter (SOM) and soil texture (sand, silt, and clay) in Northeast China. Optimal spectral indices were constructed through exhaustive band combination and correlation screening, and quantitative inversion models were established using a hybrid framework integrating Random Frog feature selection with Gaussian Process Regression (GPR) and Boosting Trees, based on synchronous ground observations. Results demonstrate strong cross-sensor consistency, with spectral indices showing significant linear correlations (${\mathrm{R}}^2>0.65$) between ZY1-02E and ZY1-02D. Furthermore, the quantitative retrieval models applied to ZY1-02E imagery achieved robust performance, with cross-sensor retrieval consistency exceeding ${\mathrm{R}}^2=0.60$ for all parameters and SOM exhibiting the highest agreement (${\mathrm{R}}^2=0.74$). These findings confirm the radiometric stability and algorithm transferability of ZY1-02E, demonstrating its capability to generate soil parameter products comparable to ZY1-02D without extensive model recalibration. The validated interoperability of the twin-satellite constellation substantially enhances temporal observation capacity during the narrow bare-soil window, effectively mitigating cloud-induced data gaps in high-latitude agricultural regions. Importantly, the enhanced monitoring framework provides a scalable technical paradigm for high-frequency hyperspectral soil mapping, offering critical spatial decision support for precision fertilization, soil degradation mitigation, and conservation tillage management in the Mollisol belt. Full article

Nitrogen and potassium are the two most essential elements for the growth of sweet potatoes. A balanced nitrogen and potassium supply is crucial for producing high-quality, high-yield sweet potatoes. This study aimed to establish an optimal nitrogen-to-potassium ratio model for diagnosing the nitrogen-to-potassium balance in sweet potato, and to achieve quantitative management of nitrogen and potassium fertilizers in sweet potato cultivation. The experimental design comprised four potassium levels (K0: 0, K1: 100, K2: 200, K3: 300 kg/ha) and four nitrogen levels (N0: 0, N1: 60, N2: 120, N3: 180 kg/ha). Biomass and nitrogen and potassium content were determined in different sweet potato organs. Bayesian modeling was employed to construct the critical plant nitrogen concentration models under varying potassium levels and the critical plant potassium concentration models under varying nitrogen levels. The results established critical nutrient concentration models for sweet potato: N_c = 3.31 DW^−0.46 and K_c = 3.39 DW^−0.47 for nitrogen and potassium, respectively. Furthermore, the critical N/K ratio was modeled as N_c/K_c = 0.976 DW^0.01. Using independent experimental data from 2020, the nitrogen–potassium nutritional balance in plants was diagnosed based on the ratio of the measured N/K ratio to the critical N/K ratio. The results demonstrated that the model exhibited satisfactory predictive performance. Accordingly, the model enables quantitative diagnosis of the in-plant N/K ratio, offering a valuable tool for assessing nutrient balance in sweet potato and providing a theoretical foundation for precise nitrogen and potassium fertilization. Full article

Rice (Oryza sativa L.) grain quality is a critical determinant of market value, consumer acceptance, and nutritional security. This multifaceted trait is governed by the dynamic interaction of genotype (G), environment (E), and management practices (M). In this review, we synthesize recent advances in understanding these multifaceted determinants. We first delineate the genetic architecture, emphasizing key genes and quantitative trait loci (QTLs) such as Wx, ALK, Chalk5, and the GS3/GW families, which control starch composition, gelatinization temperature, chalkiness, and grain dimensions, forming the foundational blueprint for quality potential. We examine how this genetic potential is influenced by environmental factors, focusing on the detrimental impacts of abiotic stresses, particularly high temperatures during grain filling and drought, which impair milling yield, increase chalkiness, and modify starch and protein profiles. Furthermore, we discuss how optimized agronomic strategies—including precision water management (e.g., alternate wetting and drying), balanced nitrogen fertilization, and targeted micronutrient (e.g., silicon) application—can mitigate these adverse effects and potentially improve specific quality parameters. Post-harvest handling is identified as the final determinant of product quality. We conclude that achieving high and stable rice quality under climate variability requires an integrated G × E × M approach. Prospects include next-generation breeding for climate-resilient quality, precision agronomy guided by real-time sensing, synergistic soil health management, and the integration of systems biology with digital agriculture to design sustainable, high-quality rice production systems. Full article

Potassium (K⁺) is a vital macronutrient for plant growth and stress resilience, with KT/HAK/KUP transporters playing a central role in its homeostasis. Although these transporters are known to influence photosynthesis, the molecular mechanisms by which fertilization promotes assimilate accumulation in peach crops remain poorly understood. In this study, 17 PpHAK genes were identified based on the peach genome and classified into four distinct clades through phylogenetic analysis, a classification further supported by conserved gene structures and motifs. Interspecific collinearity analysis revealed that transporters are highly conserved among Rosaceae species. Physiological measurements demonstrated that foliar application significantly enhanced photosynthetic capacity, as evidenced by a 33% increase in net photosynthetic rate (Pn) and improved photoelectron yield (Y(II)). At the same time, the transcript levels of the transporters PpHAK1, PpHAK5, and PpHAK9 were significantly upregulated, as confirmed by quantitative real-time RT-PCR (qRT-PCR) analysis. Furthermore, the expression of genes involved in sugar metabolism and transport, particularly PpPLT5-1, was significantly induced. Collectively, these results indicate that foliar K⁺ application enhances photosynthesis and promotes assimilate accumulation by modulating the expression of both K⁺ and sugar transporters. These findings offer a theoretical basis for optimizing nutrient management to improve fruit quality in stone fruit production. Full article

Timely and accurate soil property information is critical for sustainable agriculture and precision nutrient management. Conventional laboratory methods are accurate but costly and labor-intensive, restricting their feasibility for high-density soil mapping. Low-cost, portable near-infrared (NIR) spectroscopy presents a promising alternative for rapid, on-site, and non-destructive soil analysis. This study aimed to evaluate the potential of a low-cost, portable NIR sensor (NeoSpectra) for the quantitative prediction of key soil properties in paddy fields from Southeastern China. The target properties were soil organic matter (SOM), total nitrogen (TN), pH, and particle size fractions (clay, silt, and sand). A total of 995 soil samples were collected from representative paddy fields in the region and spectra measurements were conducted in the laboratory on air-dried samples. We developed and compared the performance of multiple machine learning algorithms, including partial least squares regression (PLSR), Cubist, random forest (RF) and memory-based learning (MBL), to build robust calibration models. The predictive models showed substantial performance for SOM and TN, indicating high accuracy (R² > 0.75, LCCC > 0.85, RPD > 2) for quantitative prediction. Predictions for pH, silt, sand, and clay were less accurate (R² of 0.48–0.53, LCCC of 0.67–0.71, RPD of 1.39–1.49), suggesting the sensor’s utility is limited to indicating general trends for these properties. Among the tested algorithms, MBL consistently provided the most accurate and robust predictions across the majority of soil properties. Our findings demonstrate that the low-cost portable NIR sensor, when coupled with appropriate machine learning algorithms, is a powerful and viable tool for the rapid and reliable estimation of critical paddy soil fertility properties (SOM and TN). This technology has significant potential to support field-level soil health monitoring, precision fertilization strategies, and sustainable land management in the agricultural systems of Southeastern China. Full article

The spatial variability of soil physicochemical properties significantly influences irrigation efficiency, nutrient availability, and the long-term sustainability of irrigated agriculture in semi-arid regions. This study aimed to quantify and model the spatial distribution of soil properties in a semi-arid irrigation district in central Mexico (Irrigation District 001 “Pabellón de Arteaga”, Aguascalientes), providing spatially explicit information for differential irrigation and fertilization management. Ninety-seven crop and four natural sampling sites were established under a stratified random design at two soil depths (0–30 and 30–60 cm). Geostatistical and machine learning models (Ordinary Kriging, OK; Generalized Additive Models, GAM; and Random Forest, RF) were applied to predict spatial patterns, and their performance was evaluated using statistical metrics. The findings reveal high spatial and vertical variability, with most properties (such as organic matter, total nitrogen, and texture) showing significant stratification with depth. In contrast, others (pH and electrical conductivity, EC) remained remarkably homogeneous vertically. Correlation patterns were identified, highlighting the negative influence of alkaline pH (≈8.0) on the availability of micronutrients (Fe²⁺ and Mn²⁺) and the positive association between EC and soluble cations (Ca²⁺, K⁺, and Na⁺). Moran’s Index confirmed significant spatial autocorrelation for most properties, reducing the effective sample size by 30–70%. The comparative evaluation of predictive models demonstrated the superiority of RF over OK and GAMs for predicting chemical properties, thanks to its ability to capture nonlinear relationships and complex interactions. However, the overall predictive performance was moderate, reflecting the multifactorial complexity of the edaphic system. This study lays the foundation for the development of an accessible, low-cost Decision Support System by providing a robust methodological framework for spatial soil characterization and contributing to more sustainable, resilient agriculture, where decision-making is based on quantitative data and predictive models. Full article

Under drip irrigation conditions, the transport pattern of soil water in the root zone directly affects the water use efficiency of crops. The type of soil matrix, initial moisture content, and irrigation water quality jointly determine the hydrodynamic process of water infiltration. However, as a special type of irrigation water, the water movement mechanism of biogas slurry under drip irrigation in soilless cultivation substrates still lacks systematic investigation. In this study, transparent soil column infiltration experiments were conducted using two types of cultivation substrates—organic (coconut coir) and inorganic (desert sand)—under controlled facility conditions. Three initial moisture contents (10%, 15%, and 20%) and two irrigation water qualities (tap water and diluted biogas slurry) were combined to form twelve treatment groups. Soil moisture sensors and visualization techniques were employed to quantitatively analyze the wetting front morphology, vertical and horizontal infiltration rates, wetting ratio, and soil moisture profile distribution under different treatments. The results showed that the initial moisture content significantly influenced the advancement pattern of the wetting front. Higher initial moisture levels promoted the transformation of the wetting front shape from a “semi-pear” form to a “hemispherical” one and reduced the rate of infiltration decline. The coconut coir substrate exhibited stronger vertical infiltration capacity and a central water aggregation characteristic, whereas the desert sand demonstrated a wider horizontal expansion range. Under low and moderate initial moisture conditions, the application of biogas slurry enhanced horizontal water diffusion and improved the uniformity of the wetted zone, with the wetting ratio increasing by more than 6% compared with high moisture conditions. In addition, the power function model provided an excellent fit for the cumulative infiltration process across all treatments (R² > 0.96), indicating its suitability for describing the water transport process in facility cultivation substrates. This study provides theoretical support for precise water and fertilizer management and the efficient utilization of biogas slurry in soilless cultivation systems. Full article

Reproductive dysfunction in captive-bred Senegalese sole (Solea senegalensis) limits aquaculture production consolidation, particularly due to reduced fertility and poor sperm quality in F1 males. To elucidate the molecular mechanisms underlying this problem, a quantitative proteomic analysis was conducted using LC–MS/MS on gonadal tissues from wild and F1 males and females. A total of 2221 proteins were identified, of which 1797 were retained after quality filtering. Comparative analyses revealed clear segregation by origin (F1 [cultivated] and wild) and sex (male and female), and 86 proteins were differentially expressed between F1 and wild males. Functional enrichment showed significant downregulation of key reproductive processes in F1 males, including sperm–egg recognition, binding of sperm to zona pellucida, and acrosome reaction, suggesting impaired gamete interaction and fertilization ability. Conversely, F1 males displayed metabolic and proteolytic pathway enrichment, which is indicative of compensatory energy demands. Protein–protein interaction network analysis identified a reproductive subnetwork dominated by zona pellucida sperm-binding proteins, which exhibited reduced connectivity in F1 males. These results demonstrate a coordinated suppression of molecular components essential for sperm–egg communication and acrosomal exocytosis, providing proteomic evidence for the systemic deregulation of the reproductive machinery in F1 fish. This study identifies potential protein biomarkers linked to reproductive performance, offering molecular targets to improve broodstock management and fertilization success in S. senegalensis aquaculture. Full article