Agricultural Non-point Source Pollution Research Articles

Study of Steroid Estrogen Loss in Soil after the Application of Composted Manure as a Fertilizer

Steroid estrogens (SEs) play a significant role as endocrine-disrupting substances, and one of their major sources is animal manure. However, there is limited information available regarding the loss of SEs in farmland soil after the application of commercial composted animal manure or fertilizers. To address this gap, our study aimed to simulate rainfall and flood irrigation scenarios and investigate the loss characteristics of SEs, as well as Chemical Oxygen Demand (COD), Total Nitrogen (TN), and Total Phosphorus (TP) in runoff from soil–manure mixtures. The results demonstrated that the loss concentrations of SEs (73.1 ng/L of the mean E2β active equivalent factor) presented a potential environmental risk. Additionally, substituting composted manure with commercial organic fertilizers lead to a significant reduction in TP (maximum 56%) and TN (maximum 24%) loss. Consequently, the application of commercial organic fertilizers offers considerable advantages in maintaining nitrogen and phosphorus fertilization efficiency while controlling SEs loss. Furthermore, our study explored the synergistic pollution mechanism among these pollutants and observed significant correlations between SEs and TN, TP, and COD loss concentrations, indicating the simultaneous occurrence and migration of these pollutants in agricultural non-point source pollution. These results provide valuable insights into the environmental risk associated with SEs from agricultural non-point sources.

Classification of Pollution Sources and Their Contributions to Surface Water Quality Using APCS-MLR and PMF Model in a Drinking Water Source Area in Southeastern China

Identifying the potential pollution sources of surface water pollutants is essential for the management and protection of regional water environments in drinking water source areas. In this study, absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models were applied to assess water quality and identify the potential pollution sources affecting the surface water quality of Xin’an River Basin. For this purpose, a 10-year (2011–2020) dataset of eight water quality indicators (including pH, EC, DO, COD, NH3-N, TN, TP, and FC) covering eight monitoring stations and 7248 monthly observations was used. The results indicated that Pukou section had the worst water quality among the eight monitoring stations, and TN was the most serious water quality index. Both the APCS-MLR and PMF models identified agricultural nonpoint source pollution, urban nonpoint source pollution and rural domestic pollution, and meteorological factors. The sum of these three sources was very close, accounting for 60% and 58%, respectively. The APCS-MLR results demonstrated that for EC, COD, and NH3-N, the major pollution sources were urban nonpoint sources and rural domestic pollution. The major contamination source of TN was agricultural nonpoint source pollution (30.4%). Meanwhile, the major pollution sources of pH, DO, TP, and FC were unidentified factors. The PMF model identified five potential sources, and pH and DO were affected by meteorological factors. NH3-N and TP were influenced mainly by agricultural nonpoint source pollution. Atmospheric deposition was the major pollution source (87.9%) of TN. FC was mostly derived from livestock and poultry breeding (88.3%). EC and COD were mostly affected by urban nonpoint sources and rural domestic pollution. Therefore, receptor models can help managers identify the major sources of pollution in watersheds, but the major factors affecting different pollutants need to be supplemented by other methods.

Assessing spatiotemporal risks of nonpoint source pollution via soil erosion: a coastal case in the Yellow River Delta, China.

Nonpoint source pollution (NPSP) has always been the dominant threat to regional waters. Based on empirical models of the revised universal soil loss equation and the phosphorus index, an NPSP risk assessment model denoted as SL-NPSRI was developed. The surface soil pollutant loss was estimated by simulating the rain-runoff topographic process, and the influence of path attenuation was quantified. A case study in the Yellow River Delta and corresponding field surveys of soil pollutants and water quality showed that the established model can be applied to evaluate the spatial heterogeneity of NPSP. NPSP usually occurs during high-intensity rainfall periods and in larger estuaries. Summer rainfall increased pollutant transport into the sea from late July to mid-August and caused estuarine dilution. Higher NPSP risks often correspond to coastal areas with lower vegetation coverage, higher soil erodibility, and higher soil pollutant concentrations. Agricultural NPSP originating from cropland significantly increase the pollutant fluxes. Therefore, area-specific land use management and vegetation coverage improvement, and temporal-specific strategies can be explored for NPSP control during source-transport hydrological processes. This research provides a novel insight for coastal NPSP simulations by comprehensively analyzing the soil erosion process and its associated pollutant loss effects, which can be useful for targeted spatiotemporal solutions.

Research on a Biofilter for a Typical Application Scenario in China: Treatment of Pesticide Residue Wastewater in Orchards

To reduce pesticide pollution and promote sustainable agricultural development in China, we designed a pilot-scale biofilter system to treat residual imidacloprid wastewater in an orchard. The biofilter system demonstrated a high rate of removal of imidacloprid from the biodegradation wastewater, with removal rates from the outlet exceeding 99% at different concentrations of pesticides. Among environmental factors, imidacloprid concentration at the inlet and biomixture significantly affected the activity of imidacloprid-degrading bacteria. The dominant microbial communities during the stable operation of the biofilter system included Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes at the phylum level and Bacillus, Methylobacter, and unclassified_f__Microbacteriaceae at the genus level. In future initiatives to improve biofilter performance and applicability, increasing attention should be paid to the dominant microbial communities, the number of biofilter units, and important environmental factors. Orchard workers in China should improve the existing treatment of residual pesticide wastewater to mitigate agricultural non-point source pollution.

Evaluating Best Management Practice Efficacy Based on Seasonal Variability and Spatial Scales

Implementing best management practices (BMPs) has proven to be an efficient method for reducing non-point source (NPS) pollutants. Agricultural NPS pollution is considered to be a major contributor to water quality impairment. This study aims to assess the variation in hydrologic and water quality outputs at field and watershed scales when BMPs are implemented using modeling approaches. The Yazoo River Watershed (YRW) is the largest watershed basin in the state of Mississippi with approximately 50% agricultural land. Runoff generated from agricultural areas carries sediments and nutrients. The Merigold watershed (MW) is a sub-basin of the YRW and a field-scale watershed with most of the land use being agriculture. It is essential to quantify the streamflow, sediment, total nitrogen (TN), and total phosphorus (TP) when BMPs are implemented. BMPs such as vegetative filter strips (VFS) and cover crops (CC) were tested in this study. The Soil and Water Assessment Tool (SWAT) model was applied to quantify the watershed’s hydrologic and water quality outputs. SWAT model accuracy assessment was performed by calibration and validation process using the Nash and Sutcliffe Efficiency Index (NSE). Model performance was satisfactory for monthly streamflow, with NSE values in the range of 0.62 to 0.81, and for daily sediments, TN, and TP load estimation, with NSE values of 0.21, 0.20, and 0.47, respectively. CC was planted after harvesting the main crop. Therefore, it is essential to quantify the seasonal reduction in pollutants. Water quality was improved after BMP implementation, and an overall decrease in streamflow, sediment, TN, and TP loads was observed for both MW and YRW during dry and wet seasons. Previous studies regarding seasonal assessments with CC implementation in the MW and YRW were limited. Therefore, the results from this study could be a unique addition to the scientific literature.

Assessment of Agricultural Non-point Source Pollution Loads Applying Improved Export Coefficient Model (IECM) : A Case Study in Taihu Lake Basin

Accurate and comprehensive estimation of agricultural non-point source pollution (ANPSP) is of vital importance in watershed pollution control, while data inaccessibility and complexity of process-based models may be main restrictions in practice. An estimation methodology framework for ANPSP was established based on improved export coefficient model (IECM) in this research. A series of updated, regional coefficients were collected and/or calculated with multiple pollution sources considered (i.e., animal husbandry, farming and forestry, fishery, and rural domestic sewage) to provide a comprehensive estimation of TN and TP pollution loads in Taihu Lake Basin. According to this model, pollution loads of TN and TP in Taihu Lake Basin were 65791.28 and 11400.38 t in 2016, respectively. Animal husbandry was main source of ANPSP, accounting for 31.05% and 66.70% for TN and TP, respectively, which requires more efficient and clean agricultural production modes and strict pollution control measures. IECM-based estimation framework may provide a reference for ANPSP management practice in large scale watershed.

“Period-area-source” hierarchical management for agricultural non-point source pollution in typical watershed with integrated planting and breeding

Agricultural non-point source (NPS) pollution threatens the aquatic environments significantly. Insufficient attention to the migration characteristics of multiple sources of pollution as well as the variability of anthropogenic interference hinders effective pollution control, particularly in watersheds with integrated planting and breeding (IPB). In this study, we proposed a distributed dual-structure empirical export model for IPB (DSEEM-IPB) that optimizes hydrological processes in pollutant transport and refines NPS load estimation from animal breeding sources to simulate agricultural NPS pollution in a typical IPB watershed. Based on the estimated NPS load, a “period-area-source” management approach for NPS pollution is proposed with a co-analysis of critical periods (CPs), critical source areas (CSAs), and critical source types (CSTs). The results show that (1) the CPs of only two months (June and July) contributed 62.9% and 56.2% of the total nitrogen (TN) and total phosphorus (TP) load respectively; (2) the CSAs in the CPs contributed 17.6% of the TN load within 14.0% of the area and 16.2% of the TP load within 13.4% of the area; (3) planting (74.4%) is the primary source of nitrogen loss while breeding (58.3%) is the primary source of phosphorus loss; and (4) optimal control measures should target planting sources in CSAs and sub-CSAs within CPs (yielding 32.4% of annual TN loads) and breeding sources in CSAs within non-CPs (yielding 22.1% of annual TP loads). This study indicates that prioritizing pollution management of critical sources from critical areas or periods can achieve efficient and targeted control of agricultural NPS pollution.

Evolutionary Trend Analysis of Agricultural Non-Point Source Pollution Load in Chongqing Based on Land Use Simulation

Analysis of the relationship between future land use change and agricultural non-point source pollution (ANPSP) evolution is vital to promoting sustainable regional development. By simulating future land use types, we can identify and analyze the evolution trend of ANPSP. This study takes Chongqing as a case study to establish an integrated solution based on the PLUS model, output coefficient model, and GIS technology. The solution can simulate data, identify trends, and identify key control areas under future development scenarios. The results show that the PLUS model can simulate land use types at the provincial scale with high accuracy, with a Kappa coefficient of around 0.9. The land use type changes show that urban expansion has occupied a large amount of cultivated land. From 2000 to 2020, the proportion of high-load areas with TN pollution load levels was 4.93%, 5.02%, and 4.73%, respectively. Under the two scenarios in 2030–2050, the number of high-load areas decreased, and the average load level decreased from west to east. Sensitivity analysis found that risk changes are more sensitive to the increase in fertilizer application. When the TN and TP output coefficients are increased, the number of towns with increased levels is greater than those with decreased levels when the output coefficients are decreased. Sensitivity analysis can better identify key pollution control areas. The areas sensitive to changes in farmers’ behavior are mainly the Hechuan District, Nanchuan District, Qijiang District, Jiangjin District, and Bishan District. This study provides data and decision-making support for rural green development and water environment improvement.

Nutrient runoff and leaching under various fertilizer treatments and pedogeographic conditions: A case study in tobacco (Nicotiana tabacum L.) fields of the Erhai Lake basin, China

To ensure water security, it is crucial to understand the characteristics of nitrogen (N) and phosphorus (P) in water and to reduce their inputs. We conducted a 2-year trial (2021–2022) in tobacco fields across three sites in the Erhai Lake basin, China, to comprehensively measure and summarize N and P loss through runoff and leaching for four fertilizer treatments: no fertilizer (CK); farmers' traditional organic fertilizer (FP); chemical fertilizer (CF); and a combination of organic and inorganic fertilizer (OIC). The highest N concentrations were detected in leachate in the early tobacco growth stage, whereas the highest P concentrations occurred in runoff in the middle growth stage. Compared to FP, the OIC treatment improved nutrient uptake in flue-cured tobacco, while also reducing N loss through runoff and leaching by 45.37% and 19.34%, and reducing P loss by 7.22% and 8.26%, respectively. Correlation analyses revealed positive relationships of nutrient loss with soil organic matter content and nutrient inputs; elevation and soil water content exhibited varying correlations with different nutrients. These findings provide a theoretical foundation for the effective management of agricultural non-point source pollution in the Erhai Lake basin and shed light on water pollution pressures worldwide.

Assessing the Influence of Agricultural Nonpoint Source Pollution on Water Quality in Central Kentucky’s Headwater Streams

This study addresses the pressing issue of nonpoint source water pollution in Kentucky, particularly associated with large-scale agriculture. Centered on the outer bluegrass region of Central Kentucky, the research examines the water quality of headwater streams during the agricultural season. The approach involves geospatial land cover classification using aerial imagery. Water quality data were collected during the agricultural growing season from May to October 2018. Land cover classification utilized ERDAS Imagine 2016 and ESRI ArcGIS 10.6 GIS software, while conventional water quality parameters were measured with a YSI ProDSS® multiparameter water probe and a Marsh-McBirney Flo-Mate 2000 flow meter. Statistical analyses show significant differences in stream water chemistry, suggesting the impact of agricultural nonpoint source pollution. Forested streams exhibited more varied conditions, indicating a potentially better environment. As agricultural land percentage increased, water chemistry variation suggested a measurable threshold for changes. Significant differences in water quality between agricultural and forested streams highlight the potential benefits of expanding riparian zones beyond regulations. Enlarging these zones is proposed as a strategy to mitigate nonpoint source pollution in Kentucky’s waterways.

Managing the water-agriculture-environment-energy nexus: Trade-offs and synergies in an arid area of Northwest China

Water is a stabilizer for production and life in the arid areas. According to research shared by the Pacific Institute and The Guardian, there were 228 incidents of water conflict globally in 2022. Therefore, comprehensive measures are necessary to achieve regional water security. This objective can be achieved by the application of interdisciplinary and professional approaches through establishing a dynamic equilibrium of the different water consumers, and trade-offs and synergies for water allocation between them. A multi-objective optimization model is proposed for managing the water-agriculture-energy-environment (WAEE) nexus in water-scarce areas. Real and integer-encoded multi-objective evolutionary algorithms (EMCMO) were applied to solving management models. Multiple objectives, such as the maximization of economic benefits from agriculture and hydropower generation, the minimization of ecological water shortages for local plants, and the minimization of environmental risks influenced by land use and food supply, were defined in the model. The model was applied to the Yanqi Basin, an important agriculture and energy producer in Xinjiang province, Northwest China. The results indicated that (1) the four-dimensional Pareto solution set has the advantage of presenting the WAEE nexus, highlighting the trade-offs and synergies between high-value crops, surface water, and groundwater, ecological water demand, hydropower generation and total nitrogen and phosphorus loading (TNP); (2) An excessive increase in the agricultural economy will increase TNP loading and agricultural non-point source pollution; (3) Optimization of WAFE for local ecosystem restoration. Optimization in a wet year, reduced agricultural economic benefits by 0.20%, and agricultural TNP loading by 13.19% while increasing ecological water by 58.33%, and hydropower generation by 3.98%; in a normal year, reduced agricultural economic benefits by 6.62%, agricultural TNP loading by 13.19% while increasing ecological water by 46.43%, and hydropower generation by 0.79%; in a dry year, reduced agricultural economic benefits by 17.19%, agricultural TNP loading by 9.72%, and hydropower generation by 16.18% while increasing ecological water by 42.86%. The trade-offs and synergies of the WAEE nexus also apply to other similar water-scarce areas.

A Comprehensive Analysis of Agricultural Non-Point Source Pollution in China: Current Status, Risk Assessment and Management Strategies

With the expansion of the scope and diversity of agricultural non-point source (AGNPS) pollution, environmental pollution in water bodies and soils has intensified, seriously affecting the quality of life of urban and rural residents. This paper presents a comprehensive analysis of the current status of AGNPS pollution from five aspects: fertilizers, pesticides, agricultural films, livestock and crop residues. The study also includes a comprehensive risk assessment of the nitrogen and phosphorus pollution, microplastic pollution, and heavy metal contamination attributed to the current AGNPS pollution in China. The findings reveal widespread nitrogen and phosphorus pollution across provinces in China, a low risk of microplastic pollution, and acute contamination with heavy metals such as As, Cd and Hg, notably in Hubei, Gansu, Liaoning, Guizhou, and Hunan. Additionally, the study reveals that in Chinese provinces currently facing severe heavy metal pollution, heavy metal contamination constitutes a significant carcinogenic risk to adult health and a serious non-carcinogenic risk to child health. Consequently, guided by the ‘3R theory’ principles of source reduction, interception, and remediation, this paper suggests an array of expanded strategies for AGNPS pollution management. These strategies are proposed to aid in the sustainable advancement of AGNPS pollution control.

Does digital village construction contribute to improving food security?

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">The issue of food security has become a global challenge, and it is therefore crucial to seek measures to ensure food security. As an emerging economic model, the digital economy is regarded as the most effective tool for the modernization of agricultural development. As a large food-producing country, China faces serious problems of agricultural non-point source pollution and food loss and waste. The purpose of this study is to explore the impact of digital village construction on food security. We measure the level of digital village construction and food security by entropy method and use the spatial Durbin model to analyze them. In this paper, we found that digital village construction facilitates food security not only in the region but also in neighboring regions. In addition, we found that the promotion effect is significant in main grain producing regions, while the opposite result is found in non-main grain producing regions. We hold that the application of the digital economy in the countryside has played an obvious role in promoting food security in main grain producing provinces, and provides important experimental evidence for reference to ensure food security in the future.</span></p>

Prediction of agricultural nonpoint source pollution in highly urbanised areas based on shared socioeconomic pathways: a case study of Taihu Lake Basin

Prediction of agricultural nonpoint source pollution in highly urbanised areas based on shared socioeconomic pathways: a case study of Taihu Lake Basin

Study On Spatial Variations of Surface Water Quality Vulnerable Zones in Baitarani River Basin, Odisha, India

The stated goal of the research is to investigate the surface water quality of the Baitarani River in Odisha to ascertain its compatibility for various uses. Large, complex datasets generated during the one-year (2021-2022) monitoring program were collected from 13 locations and encompassed 22 parameters. To examine temporal and spatial fluctuations in and to interpret these datasets, MCDMs like TOPSIS and the Entropy-based Water Quality Index (EWQI) were utilized. The physical and chemical outcomes of the current experiment were compared to WHO standards. According to the analysis’s results, turbidity and total coliform (TC) are indicators that have a greater impact on water quality in all locations during both seasons and are directly linked to home and agricultural non-point source pollution. As per EWQI interpretation, 30.77 % of the observations in PRM and POM fall under the poor category. The findings showed how anthropogenic activities have harmed St. 8, 11, 12, and 13 and require effective management. A quantifiable approach was also carried out to decide the efficacy of TOPSIS. Farming attributes, including SAR, % Na, RSC, MR, KI, and PI, were estimated to delineate the agriculturally practicable zones. This work can offer a reference database for the betterment of water quality.

Dissolved Oxygen Forecasting for Lake Erie’s Central Basin Using Hybrid Long Short-Term Memory and Gated Recurrent Unit Networks

Dissolved oxygen (DO) concentration is a pivotal determinant of water quality in freshwater lake ecosystems. However, rapid population growth and discharge of polluted wastewater, urban stormwater runoff, and agricultural non-point source pollution runoff have triggered a significant decline in DO levels in Lake Erie and other freshwater lakes located in populated temperate regions of the globe. Over eleven million people rely on Lake Erie, which has been adversely impacted by anthropogenic stressors resulting in deficient DO concentrations near the bottom of Lake Erie’s Central Basin for extended periods. In the past, hybrid long short-term memory (LSTM) models have been successfully used for the time-series forecasting of water quality in rivers and ponds. However, the prediction errors tend to grow significantly with the forecasting period. Therefore, this research aimed to improve the accuracy of DO forecasting models by taking advantage of Lake Erie’s real-time water quality (water temperature and DO concentration) monitoring network to establish temporal and spatial links between adjacent monitoring stations. We developed hybrid LSTM models that combine LSTM, convolutional neuron network LSTM (CNN-LSTM), hybrid CNN with gated recurrent unit (CNN-GRU) models, and convolutional LSTM (ConvLSTM) to forecast near-bottom DO concentrations in Lake Erie’s Central Basin. These hybrid LSTM models improve their capacity to handle complicated datasets with spatial and temporal variability. These models can serve as accurate and reliable tools for forecasting DO concentrations in freshwater lakes to help environmental protection agencies better access and manage the health of these vital ecosystems. Following analysis of a 21-site Lake Erie dataset for 2020 and 2021, the ConvLSTM model emerged as the most accurate and reliable, boasting an MSE of 0.51 mg/L, MAE of 0.42 mg/L, and an R-squared of 0.95 over the 12 h prediction range. The model foresees future hypoxia in Lake Erie. Notably, the temperature near site 713 holds significance for Central Basin DO forecasting in Lake Erie, as indicated by outcomes derived from the Shapley additive explanations (SHAP).

Economic driving characteristics of agricultural non-point source pollution and prevention suggestions: a case study from Shandong province in China

Agricultural non-point source pollution (ANSP) constraints on the development of the agricultural economy, grasp the interaction between the two, and develop targeted prevention and control policies in accordance with the evolution of different stages of the characteristic is essential. This study examines the economic characteristics of ANSP in Shandong Province to propose targeted countermeasures. Selected the time series data of Shandong Province from 2000 to 2020 to test the Environmental Kuznets Curve (EKC) characteristics between ANSP and agricultural economic growth (AEG), and the Vector Error Correction model (VECM) is used further to explore the dynamic relationship and mechanism. We validate the existence of an Inverted U-shaped EKC in Shandong Province, and the inflection points of pesticides, fertilizers, and agricultural films were reached in 2007 and 2008. The changes in pesticide loss and fertilizer loss have a positive impact on the dynamics of AEG in the short term, but there is an incremental inhibition in the long term. The dynamic effect of the impulse response diagram between the amount of agricultural film residues and AEG all shows a positive impact. The average explanatory contribution of pesticide loss, fertilizer loss, and agricultural film residue to AEG decreases by 12.14%, 7.7%, and 3%, respectively. Therefore, policy suggestions are proposed to realize the coordinated development of ANSP and economic development.

Factors Influencing Willingness of Farmers to Pay for Agricultural Non-point Source Pollution Control Based on Distributed Cognitive Theory

The analysis of the willingness of individual farmers to cover costs is an important basis for measuring the economic value of agricultural non-point pollution management， and determining the ecological and economic value of rural surface pollution control is a necessary measure to internalize the externalities of agricultural production. Based on the analysis of the hierarchy of factors influencing the cognition of farmers， this study constructed a theoretical framework based on distributed cognition theory to analyze their willingness to pay for agricultural non-point source pollution control from the perspective of individual farmers. On the basis of this framework， we used the Double-Hurdle model to empirically test the overall process of farmers'willingness to pay and their willingness to pay the amount for agricultural non-point source pollution control by combining 531 microscopic research datapoints in Guanzhong， Shaanxi Province. The results showed that： ① the number of farmers with willingness to pay for agricultural non-point source pollution control was 267， accounting for 50.30% of the total sample， and the average value of willingness to pay was 1 469.77 yuan·hm-2； the total economic value of agricultural non-point source pollution control in Shaanxi Province in 2020 was estimated to be 5.791 billion yuan based on the expected value of the willingness to pay level of the research sample. ② Farmers'willingness to pay for agricultural non-point source pollution control was influenced by the combined effects of personal， regional， and cultural forces， and the effects of each dimension were similar； farmers' willingness to pay for agricultural non-point source pollution control was mainly influenced by the cultural force factor， and the effects of personal and regional forces were very limited. ③ The results of the regressions by income level showed that personal and cultural strengths had a significant impact on the willingness to pay among the low-income group but did not contribute to the increase in the willingness to pay.

Manure derived hydrochar reduced phosphorus loss risk via an alteration of phosphorus fractions and diversified microbial community in rice paddy soil

Phosphorus (P) loss caused by the irrational use of manure organic fertilizer has become a worldwide environmental problem, which has caused a potential threat to water safety and intensified agricultural non-point source pollution. Hydrothermal carbonization is method with a low-energy consumption and high efficiency to deal with environmental problems. Application of pig manure-derived hydrochar (PMH) to soil exhibited potential of sustainable development compared with the pristine pig manure (PM). However, the effects of PMH on the distribution of P among the fractions/forms and the interaction between microorganisms and P forms and its relevance to the potential loss of P in paddy fields has not been clarified. Therefore, in this study, a soil column experiment was conducted using the untreated soil (control), and the PM, PMH1 (PMH derived at 180 °C), and PMH2 (PMH derived at 260 °C) treated soils (at the dose of 0.05 %) and rice was cultivated to investigate the effects of PM and PMH on the P fractions, mobilization, ad potential loss via the induced changes on soil microbial community after a complete growing season of rice. The trend of P utilization was evaluated by P speciation via continuous extraction and 31P NMR. The addition of PMH reduced the proportion of residual P in soil by 23.8–26.3 %, and increased the proportion of HCl-P and orthophosphate by 116.2–158.6 % and 6.1–6.8 % compared to PM. The abundance of gcd gene developed after the application of PMH2, which enhanced the mobile forms of soil P utilization via secreting gluconic acid. The network diagram analysis concluded that the changes in various P forms were mainly related to Proteobacteria, Bacteroides, Firmicutes and Acidobacteria. The results illustrated that PMH mitigate the potential risk of P loss more than PM by altering P fractions and affecting soil microbial community.

Retention Efficiencies of Vegetative Filter Strips in Reducing Agricultural Nonpoint Source Pollution in Jianghan Plain: Experiments and VFSMOD Modeling

Retention Efficiencies of Vegetative Filter Strips in Reducing Agricultural Nonpoint Source Pollution in Jianghan Plain: Experiments and VFSMOD Modeling