Search Results (249)

Fertilization is a critical measure for vegetation restoration and ecological reconstruction in degraded grasslands. However, little is known about the long-term effects of different combinations of nitrogen (N), phosphorus (P), potassium (K) on plant and microbial communities in degraded grasslands. This study conducted a four-year (2017–2020) N, P, K addition experiment in the Khorchin Grassland, a degraded typical grassland located in Zhalute Banner, Tongliao City, Inner Mongolia, to investigate the effects of fertilization treatment on plant functional groups and microbial communities after grazing exclusion. Our results showed that the addition of P, NP, and NPK compound fertilizers significantly increased aboveground biomass of the plant community, which is mainly related to the improvement of nutrient availability to promote the growth of specific plant functional groups, especially annual and biennial plants and perennial bunchgrasses. However, the addition of N, P, and NP fertilizers significantly reduced the species diversity of the plant community. At the same time, the addition of N, P, and NP fertilizers and the application of N and NP significantly reduced fungal species diversity but had no significant effect on soil bacteria. Our study provides new insights into the relationships between different types of fertilization and plant community productivity and biodiversity in degraded grasslands over four years of fertilization, which is critical for evaluating the effect of fertilization on the restoration of degraded grassland. Full article

The arid-hot valleys of Sichuan Province contain extensive engineered gravel deposits, where ecological restoration has become the predominant remediation strategy. Accelerating vegetation recovery and continuously improving productivity are important prerequisites for the protection of regional biodiversity. We employed fertilization and sowing cultivation to facilitate ecological restoration. We have conducted continuous ecological experiments for two years using the following experimental treatments, covering indigenous soil, adding organic fertilizer, and applying compound fertilizer and organic fertilizer, with six types of sowing established under each soil treatment: monoculture and pairwise mixed cropping utilizing Elymus dahuricus (EDA), Dactylis glomerata (DGL), and Medicago sativa (MSA). Through the analysis of variance and the calculation of effect factors, our results indicated that compound fertilizer and organic fertilizer adding significantly improved vegetation cover and increased aboveground biomass, and the highest productivity was observed in the mixed sowing treatment of EDA and MSA. The effect coefficient model analysis further showed that the combination of EDA and MSA resulted in the highest selection and compensation effects on aboveground productivity. Two potential mechanisms drive enhanced productivity in mixed grasslands: the strengthening of the selection effect via increased legume nitrogen fixation, and the enhancement of the compensation effect through niche differentiation among species. Full article

Semi-natural grasslands are some of the most species-rich habitats in Europe and provide important ecosystem services such as biodiversity conservation, carbon sequestration and soil fertility maintenance. This study investigates how different intensities of grassland management affect the composition of functional groups and soil chemical properties. Five grassland management systems were analyzed: Cut3—three cuts per year; LGI—low grazing intensity; CG—combined cutting and grazing; Cut4—four cuts per year; and HGI—high grazing intensity. The functional groups assessed were grasses, legumes and forbs, while soil samples from three depths (0–10, 10–20 and 20–30 cm) were analyzed for their chemical properties (soil organic carbon—SOC; soil total nitrogen—STN; inorganic soil carbon—SIC; soil organic matter—SOM; potassium oxide—K₂O; phosphorus pentoxide—P₂O₅; C/N ratio; and pH) and physical properties (volumetric soil water content—VWC; bulk density—BD; and porosity—POR). The results showed that less intensive systems had a higher proportion of legumes, while species diversity, as measured via the Shannon index, was the highest in the Cut4 system. The CG system tended to have the highest SOC and STN at a 0–10 cm depth, with a similar trend observed for SOC_stock at a 0–30 cm depth. The Cut4, HGI and CG systems also had an increased STN_stock. Both grazing systems had the highest P₂O₅ content. A tendency towards a higher BD was observed in the top 10 cm of soil in the more intensive systems. Choosing a management strategy that is tailored to local climate and site conditions is crucial for maintaining grassland stability, enhancing carbon sequestration and promoting long-term sustainability in the context of climate change. Full article

Permanent grasslands play a crucial role in ruminant nutrition, providing cost-effective and nutritionally rich forage. Their effective management is essential for improving agricultural productivity and sustainability. This review examines factors affecting forage quality, including environmental conditions, botanical composition, conservation methods, and fertilization strategies. The impact of grassland management practices, such as cutting frequency, grazing systems, and soil fertility enhancement, on forage nutritional value is discussed. Advances in breeding, including genomic selection and molecular techniques, offer opportunities to improve digestibility and resistance to environmental stress. Furthermore, conservation methods, including haymaking and silage production, significantly influence forage quality. Special attention is given to the role of legumes and multi-species swards in enhancing protein content and mineral composition. The review highlights that optimizing forage quality requires an integrated approach, combining agronomic practices, genetic improvements, and sustainable management strategies. Future research should focus on developing resilient forage systems that maintain high nutritional value while adapting to changing climatic conditions. Full article

Agricultural production in Northwest China is widely constrained by residual plastic film pollution, excessive greenhouse gas emissions, and low productivity. Integrating biodegradable film with controlled-release nitrogen fertilizer offers a promising approach to optimize crop management, enhance yield, and improve environmental outcomes. In this study, three planting patterns (conventional flat planting, FP; ridge mulching with biodegradable film, BM; and ridge mulching with conventional plastic film, PM), two nitrogen fertilizer types (urea, U, and controlled-release nitrogen fertilizer, C), and four nitrogen application rates (0, 80, 160, and 240 kg·hm⁻²) were applied to systematically investigate their effects on alfalfa yield and N₂O emissions from grasslands. The results showed that BM and PM increased alfalfa yield by 23.49% and 18.65%, respectively, compared to FP, while C increased yield by 8.46% compared to urea. The highest yield (24.84 t·hm⁻²) was recorded under the BMC2 treatment, which was 97.11% higher than that of FPN0. N₂O emission flux and cumulative emissions increased with nitrogen application rate. Compared with U, C reduced cumulative N₂O emissions and greenhouse gas emission intensity (GHGI) by 23.89% and 25.84%, respectively. Compared to PM, BM reduced cumulative N₂O emissions and GHGI by 11.58% and 20.15%, respectively. Principal component analysis indicated that the combination of ridge mulching with biodegradable film and 160 kg·hm⁻² of C was optimal for simultaneously increasing alfalfa yield and reducing N₂O emissions, making it a suitable planting–fertilization strategy for the Yellow River irrigation district in Gansu and similar ecological regions. Full article

Grassland biomass yield reflects a complex interaction of management intensity and environmental factors, yet quantifying the relative role of practices such as mowing and fertilization remains challenging. In this study, we introduce a multi-class machine learning framework to predict above-ground biomass on 150 permanent grassland plots across eight years (2009–2016) in Germany’s Biodiversity Exploratories and to evaluate the influence of key management variables. Following rigorous data cleaning, imputation of missing nitrogen values, feature standardization, and encoding of categorical practices, we trained CatBoost classifiers optimized via Bayesian hyperparameter search and mitigated class imbalance with ADASYN oversampling. We assessed model performance under binary, three-class, four-class, and five-class quantile-based categorizations, achieving test accuracies of 0.76, 0.57, 0.42, and 0.38, respectively. Across all schemes, mowing frequency and mineral nitrogen input emerged as the dominant predictors, while secondary variables such as drainage and conditioner use contributed as well. These results demonstrate that broad biomass categories can be forecast reliably from standardized management records, whereas finer distinctions necessitate additional environmental information or automated sensing to capture nonlinear effects and reduce reporting bias. This work shows both the potential and the limits of machine learning for informing sustainable grassland management and explainability thereof. Frequent mowing and higher mineral nitrogen inputs explained most of the predictable variation, enabling a 76% accurate separation of low and high biomass categories. Predictive accuracy fell below 60% for finer class resolutions, indicating that management records alone are insufficient for detailed yield forecasts without complementary environmental data. Full article

The Qinghai–Tibet Plateau, a critical ecological barrier and major livestock region, faces deteriorating grasslands and rising forage demand under its harsh alpine climate. Oat (Avena sativa L.), valued for its cold tolerance, rapid biomass accumulation, and ability to thrive in nutrient-poor soils, can expand winter feed reserves and partly alleviate grazing pressure on native rangelands. However, genetic improvement alone has not been sufficient to address the environmental challenges. This issue is particularly severe in the Qaidam Basin, where soil salinization, characterized by high pH, poor soil structure, and low nutrient availability, significantly limits crop performance. Rhizosphere growth-promoting bacteria (PGPR) are environmentally friendly biofertilizers known to enhance crop growth, yield, and soil quality, but their application in the saline soil of the Qaidam Basin remains limited. We evaluated two PGPR application rates (B1 = 75 kg hm⁻² and B2 = 150 kg hm⁻²) on ‘Qingtian No. 1’ oat, assessing plant growth, soil physicochemical properties, and rhizosphere microbial communities. The results indicated that both treatments significantly increased oat productivity, raised the comprehensive growth index, augmented soil organic matter, and lowered soil pH; B1 chiefly enhanced above-ground biomass and fungal community stability, whereas B2 more strongly promoted root development and bacterial community stability. Structural equation modeling showed that PGPR exerted direct effects on the comprehensive growth index and indirect effects through soil and microbial pathways, with soil properties contributing slightly more than microbial factors. Notably, rhizosphere organic matter, fungal β-diversity, and overall microbial community stability emerged as positive key drivers of the comprehensive growth index. These findings provide a theoretical basis for optimizing PGPR dosage in alpine forage systems and support the sustainable deployment of microbial fertilizers under saline soil conditions in the Qaidam Basin. Full article

Groundwater nitrate (NO₃⁻) contamination has emerged as a critical global environmental issue, posing serious human health risks. This study systematically investigated the hydrochemical processes, sources of NO₃⁻ pollution, the impact of land use on NO₃⁻ pollution, and drinking water safety in an urban area of southwestern China. Thirty-one groundwater samples were collected and analyzed for major hydrochemical parameters and dual isotopic composition of NO₃⁻ (δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻). The groundwater samples were characterized by neutral to slightly alkaline nature, and were dominated by the Ca-HCO₃ type. Hydrochemical analysis revealed that water–rock interactions, including carbonate dissolution, silicate weathering, and cation exchange, were the primary natural processes controlling hydrochemistry. Additionally, anthropogenic influences have significantly altered NO₃⁻ concentration. A total of 19.35% of the samples exceeded the Chinese guideline limit of 20 mg/L for NO₃⁻. Isotopic evidence suggested that primary sources of NO₃⁻ in groundwater include NH₄⁺-based fertilizer, soil organic nitrogen, sewage, and manure. Spatial distribution maps indicated that the spatial distribution of NO₃⁻ concentration correlated strongly with land use types. Elevated NO₃⁻ levels were observed in areas dominated by agriculture and artificial surfaces, while lower concentrations were associated with grass-covered ridge areas. The unabsorbed NH₄⁺ from nitrogen fertilizer entered groundwater along with precipitation and irrigation water infiltration. The direct discharge of domestic sewage and improper disposal of livestock manure contributed substantially to NO₃⁻ pollution. The nitrogen fixation capacity of the grassland ecosystem led to a relatively low NO₃⁻ concentration in the ridge region. Despite elevated NO₃⁻ and F⁻ concentrations, the entropy weighted water quality index (EWQI) indicated that all groundwater samples were suitable for drinking. This study provides valuable insights into NO₃⁻ source identification and hydrochemical processes across varying land-use types. Full article

Understanding the processes of organic nitrogen (N) mineralization to ammonium (NH₄⁺) and NH₄⁺ oxidation to nitrate (NO₃⁻), which, together, supply soil inorganic N (the sum of NH₄⁺ and NO₃⁻), is of great significance for guiding the restoration of degraded ecosystems. This study used space-for-time substitution to investigate the dynamic changes in the rates of organic N mineralization (M_Norg) and nitrification (O_NH4) in soil at different vegetation restoration stages. Soil samples were collected from grassland (3–5 years), shrub-grassland (7–8 years), early-stage shrubland (15–20 years), late-stage shrubland (30–35 years), early-stage woodland (45–50 years), and late-stage woodland (70–80 years) in the subtropical karst region of China during the dry (December) and rainy (July) seasons. The M_Norg and O_NH4 were determined using the ¹⁵N labeling technique. The soil microbial community was determined using the phospholipid fatty acid method. Soil organic carbon (SOC), total nitrogen (TN), NH₄⁺, NO₃⁻, and inorganic N contents, as well as the soil moisture content (SMC) were also measured. Our results showed that SOC and TN contents, and the SMC, as well as microbial community abundances increased markedly from grassland to the late-stage shrubland. Especially in the late-stage shrubland, the abundance of the total microbial community, bacteria, fungi, actinomycetes, and AMF in soil was significantly higher than other restoration stages. These results indicate that vegetation restoration significantly increased soil nutrient content and microbial community abundance. From grassland to the late-stage shrubland, the soil NH₄⁺, NO₃⁻, and inorganic N contents increased significantly, and the NH₄⁺:NO₃⁻ ratios changed from greater than 1 to less than 1, indicating that vegetation restoration significantly influenced soil inorganic N content and composition. As restoration progressed, the M_Norg and O_NH4 increased significantly, from 0.04 to 3.01 mg N kg⁻¹ d⁻¹ and 0.35 to 2.48 mg N kg⁻¹ d⁻¹ in the dry season, and from 3.26 to 7.20 mg N kg⁻¹ d⁻¹ and 1.47 to 10.7 mg N kg⁻¹ d⁻¹ in the rainy season. At the same vegetation restoration stage, the M_Norg and O_NH4 in the rainy season were markedly higher than those in the dry season. These results indicate that vegetation restoration and seasonal variations could significantly influence M_Norg and O_NH4. Correlation analysis showed that the increase in M_Norg during vegetation restoration was mainly attributed to the increase in SOC and TN contents, as well as the total microbial community, bacterial, fungal, actinomycetes, and AMF abundances, and that the increase in O_NH4 was mainly attributed to the increase in M_Norg and the decrease in the F: B ratio. Moreover, the M_Norg and O_NH4 showed a strong positive correlation with inorganic N content. This study clarifies that vegetation restoration in karst regions could significantly increase M_Norg and O_NH4 through enhancing soil carbon and N contents, as well as microbial community abundances, thereby increasing the available soil N supply, which could provide a theoretical basis for soil fertility regulation in future rocky desertification management. Full article

The rapid advancement of unmanned aerial vehicle (UAV) technology has enabled the coordinated operation of multi-UAV systems, offering significant applications in agriculture, logistics, environmental monitoring, and disaster relief. In agriculture, UAVs are widely utilized for tasks such as ecological restoration, crop monitoring, and fertilization, providing efficient and cost-effective solutions for improved productivity and sustainability. This study addresses the collaborative task allocation problem for multi-UAV systems, using ecological grassland restoration as a case study. A multi-objective, multi-constraint collaborative task allocation problem (MOMCCTAP) model was developed, incorporating constraints such as UAV collaboration, task completion priorities, and maximum range restrictions. The optimization objectives include minimizing the maximum task completion time for any UAV and minimizing the total time for all UAVs. To solve this model, a deep reinforcement learning-based seagull optimization algorithm (DRL-SOA) is proposed, which integrates deep reinforcement learning with the seagull optimization algorithm (SOA) for adaptive optimization. The algorithm improves both global and local search capabilities by optimizing key phases of seagull migration, attack, and post-attack refinement. Evaluation against five advanced swarm intelligence algorithms demonstrates that the DRL-SOA outperforms the alternatives in convergence speed and solution diversity, validating its efficacy for solving the MOMCCTAP. Full article

Phosphorus (P) and silicon (Si) could profoundly affect the net primary productivity (ANPP) of grassland ecosystems. However, how ecosystem biomass will respond to different Si addition, especially under a concurrent increase in P fertilization, remains limited. With persistent demand for grassland utilization, there is a need to enhance and sustain the productivity of grasslands on the Qinghai–Tibet Plateau. Three P addition rates (0, 400, 800, and 1200 kg Ca(H₂PO₄)₂ ha⁻¹ yr⁻¹) without Si and with Si (14.36 kg H₄SiO₄ ha⁻¹ yr⁻¹) were applied to alpine grassland on the Qinghai–Tibet Plateau to evaluate the responses of aboveground biomass and the underlying mechanisms linking to structural carbon composition and physiological traits of grasses and forbs. Our results show that the application of Si significantly reduced the lignin, cellulose, hemicellulose, and total phenol contents of both grasses and forbs. Additionally, the addition of P, Si, and phosphorus and silicon (PSi) co-application significantly increased the net photosynthetic rate (Pn) and light use efficiency (LUE) of grasses and forbs. Moreover, Si promoted the absorption of N and P by plants, resulting in significant changes in the Si:C, Si:P, and Si:N ratios and increasing the aboveground biomass. Our findings suggest that Si can replace structural carbohydrates and regulate the absorption and utilization of N and P to optimize the photosynthetic process of leaves, thereby achieving greater biomass. In summary, Si supplementation improves ecosystem stability in alpine meadows by optimizing plant functions and increasing biomass accumulation. Full article

High-latitude regions store substantial amounts of soil organic matter (SOM). Icelandic volcanic soils have exceptional capabilities for SOM accumulation, but recent changes in land use can significantly impact it. Water-extractable organic matter (WEOM) represents a labile SOM pool and serves as a reliable index of SOM dynamics. We assessed the stable carbon (C), stable nitrogen (N), and WEOC (water-extractable organic carbon), as well as WETN (water-extractable total nitrogen), concentrations in soils under different land uses—semi-natural habitats (tundra and wetland) and human-managed areas (intensively and extensively grazed pasturelands and formerly and presently fertilized meadows)—in southeastern Iceland. The results suggest that human-managed sites contain more total C and N but less WEOM per unit of total C or N than semi-natural habitats, except for wetlands. Wetlands exhibited the highest WEOM content. Extensive pasturelands and fertilized meadows are becoming more common in local ecosystems, highlighting the direction of changes in Icelandic grasslands management. Full article

The integration of the Internet of Things (IoT) and artificial intelligence (AI) has reshaped modern agriculture by enabling precision farming, real-time monitoring, and data-driven decision-making. This systematic review, conducted in accordance with the PRISMA methodology, provides a comprehensive overview of recent advancements in smart sensing technologies for arable crops and grasslands. We analyzed the peer-reviewed literature published between 2020 and 2024, focusing on the adoption of IoT-based sensor networks and AI-driven analytics across various agricultural applications. The findings reveal a significant increase in research output, particularly in the use of optical, acoustic, electromagnetic, and soil sensors, alongside machine learning models such as SVMs, CNNs, and random forests for optimizing irrigation, fertilization, and pest management strategies. However, this review also identifies critical challenges, including high infrastructure costs, limited interoperability, connectivity constraints in rural areas, and ethical concerns regarding transparency and data privacy. To address these barriers, recent innovations have emphasized the potential of Edge AI for local inference, blockchain systems for decentralized data governance, and autonomous platforms for field-level automation. Moreover, policy interventions are needed to ensure fair data ownership, cybersecurity, and equitable access to smart farming tools, especially in developing regions. This review is the first to systematically examine AI-integrated sensing technologies with an exclusive focus on arable crops and grasslands, offering an in-depth synthesis of both technological progress and real-world implementation gaps. Full article

The Autonomous Province of Vojvodina, Serbia’s most agriculturally developed region, lies within the fertile Pannonian plain. Decades of agricultural intensification have transformed its landscape into a near continuous expanse of arable land, largely devoid of natural elements such as trees, shrubs, or non-crop vegetation. These simplified agroecosystems support very low biodiversity, contradicting the key principles of sustainable agricultural development. To assess farmers’ willingness to support more ecologically sound landscape practices, a survey was conducted of 400 farmers across Vojvodina. The results revealed limited openness to change; i.e., most respondents expressed a low interest in all three offered interventions: (a) introducing landscape elements, (b) fallowing, (c) converting arable land to grassland. This resistance reflects a prevailing productivist mindset in which farmers perceive themselves as producers of food, raw materials, and energy. Within this view, a neat, highly cultivated landscape is perceived as a hallmark of professionalism and success. These findings underscore the importance of developing context-sensitive policies and educational efforts that align sustainability goals with farmers’ values and economic realities. Full article

Water security is a basic requirement of a region’s residents and also an important point of discussion worldwide. The middle route of the south-to-north water diversion project (MR-SNWDP) represents the most extensive inter-basin water allocation scheme globally. It is the major water resource for the Beijing–Tianjin–Hebei region, and its security is of great significance. In this study, 28 indicators including society, nature, and economy were selected from the water sources of the MR-SNWDP from 2000 to 2017. According to the Drivers-Pressures-States-Impact-Response (DPSIR) framework principle, the entropy weight method was used for weight calculation, and the comprehensive evaluation method was used for evaluating the water security of the water sources of the MR-SNWDP. This study showed that the total loss of nonpoint source pollution (NPSP) in the water source showed a trend of slow growth, except in 2007. Over the past 18 years, the proportion of pollution from three NPSP sources, livestock, and poultry (LP) breeding industry, planting industry, and living sources, were 44.56%, 40.33%, and 15.11%, respectively. The main driving force of water security in all the areas of the water source was the total net income per capita of farmers. The main pressure was the amount of LP breeding and the amount of fertilizer application. The largest impact indicators were NPSP gray water footprint and soil erosion area, and water conservancy investment was the most effective response measure. Overall, the state of the water source safety was relatively stable, showing an overall upward trend, and it had remained at Grade III except for in 2005, 2006, and 2011. The state of water safety in all areas except Shiyan City was relatively stable, where the state of water safety had fluctuated greatly. Based on the assessment findings, implications for policy and decision-making suggestions for sustainable management of the water sources of the MR-SNWDP resources are put forward. Agricultural cultivation in water source areas should reduce the application of chemical fertilizers and accelerate the promotion of agricultural intensification. Water source areas should minimize retail livestock and poultry farming and promote ecological agriculture. The government should increase investment in water conservancy and return farmland to forests and grasslands, and at the same time strengthen the education of farmers’ awareness of environmental protection. The evaluation system of this study combined indicators such as the impact of agricultural nonpoint source pollution on water bodies, which is innovative and provides a reference for the water safety evaluation system. Full article