Agricultural Fertilizer Application Research Articles

Effect on biomass production and phosphorus use efficiency of maize by using citric acid amended di-ammonium phosphate fertilizer

Phosphorus (P) is a critical factor influencing maize (Zea mays L.) development and yield. To explore the influence of P on maize growth, a pot experiment was conducted during the summer of 2021. There were five treatments T1 = No DAP/CA, T2 = 5 ml (80 mg kg−1 soil) DAP, T3 = 5 ml (120 mg kg−1 soil) DAP, T4 = 5 ml (80 mg kg−1 soil) DAP and 2 mM citric acid, T5 = 5 ml (120 mg kg−1 soil) DAP and 2 mM citric acid. Citric acid-amended P fertilizers (i.e., CA and DAP) exhibited significant effects (p < 0.05) on all the parameters studied in comparison to the sole application of DAP. Among phosphorus fertilized treatments, the DAP 120 + CA produced significantly higher biomass yield (16.37 g pot−1), while sole DAP 80 produced lower biomass (10.61 g pot−1); the DAP 120 + CA produced significantly higher plant length (34.273 cm), while sole DAP 80 produced lower plant length (30.467 cm); DAP 80 + CA produced significantly higher phosphorus recovery (25.583%), while sole DAP 120 produced lower phosphorus recovery (19.863%); DAP 120 + CA showed significantly higher phosphorus use (69.565 mg plant-1), while sole DAP 80 showed lower phosphorus use (39.154 mg plant-1); the highest total PUE (40.503%) was recorded in DAP 120 + CA treatment, while sole DAP 80 treatments showed lower PUE (22.743%); and the highest PCN (4.25 mg g-1) was recorded in DAP 120 + CA treatment while sole DAP 80 treatments showed lower PCN (3.69 mg g-1). In conclusion, the use of citric acid-amended P fertilizers (CA and DAP) significantly improved all parameters studied, compared to the sole application of DAP. The DAP 120 + CA treatment showed the highest values for biomass yield, plant length, phosphorus recovery, phosphorus use, total PUE, and PCN, while the sole DAP 80 treatment showed lower values for these parameters. The findings suggest that growing maize using citric acid-amended DAP could be more beneficial to get a higher crop yield of maize. Thus, fertilizer application in maize agriculture can be a technique for boosting both economic and environmental profitability.

Analysis of Influencing Factors on Grain Output in Hebei Province Based on Multiple Linear Regression Analysis

Food problem is one of the major problems facing the world today. Only with food can people feel at ease. In this paper, the influence of multiple linear regression analysis on grain output was carried out according to the six indicators of total power of agricultural machinery, effective irrigation area, conversion amount of agricultural fertilizer application, pesticide use, sown area of grain crops and affected area in Hebei Province from 2000 to 2021. Through a series of fitting tests, it was found that the total power of agricultural machinery, the effective irrigated area, the sown area of grain crops and the affected area would all have a significant impact on the grain output of Hebei Province, and the test results were conducive to further guiding the general direction of grain yield increase in Hebei.

Assessing environmental impacts of chemical fertilizers and organic fertilizers in Sri Lankan paddy fields through life cycle analysis

The application of fertilizer is a very important activity in paddy cultivation as it is one of the yield-determining factors. But the use of chemical fertilizer is a matter of debate as it is suspected as a cause of many prevailing health problems and comparatively large amounts of environmental pollution. Therefore, the focus of this study was to carry out a proper life cycle analysis on the application of chemical and agricultural waste-based organic fertilizers considering all the important steps such as collection of raw material, manufacturing, transportation, and application fields. The integration application of chemical and organic fertilizers was considered with two different ratios. Characterized results reveal that chemical fertilizer application has the highest impact on all selected categories, while organic fertilizer application has lower impact.The combination of chemical and organic fertilizers shows that reducing the proportion of chemical fertilizers reduces environmental impacts. Additionally, the study identifies the processes that significantly contribute to each impact category, such as urea production for climate change and freshwater ecotoxicity, and composting and field application for human toxicity and terrestrial acidification. Normalized results show that switching to organic fertilizers could reduce up to 82.4% of the current environmental impact. Therefore, an integrated approach will give more benefits in many ways. These findings provide valuable insights for policymakers and farmers in making informed decisions regarding sustainable fertilizer management.

Analysis and Prediction of the Impact of Socio-Economic and Meteorological Factors on Rapeseed Yield Based on Machine Learning

Rapeseed is one of China’s major oil crops, and accurate yield forecasting is crucial to the growth of the rapeseed industry and the country’s food security. In this study, the data on natural and socio-economic factors from 2001 to 2020 and the yield of rapeseed in China were used as the data basis. The Pearson correlation coefficient was used to analyze the relationship between the influencing factors and the yield of rapeseed, and the prediction effect of four machine learning models (linear regression (LR), decision tree (DTR), random forest (RF), and support vector machine (SVM)) on the yield of rapeseed was compared in China’s main rapeseed-producing area. The results demonstrate that the yield of rapeseed in China showed an increasing trend, but fluctuated greatly. Rural electricity consumption, gross agricultural production, the net amount of agricultural fertilizer application, effective irrigation area, total power of agricultural machinery, and consumption of agricultural plastic film had a positive effect on the increase in rapeseed yield. However, due to the impact of climate change and disasters, the yield of rapeseed has had significant fluctuations. A Pearson correlation analysis showed that socio-economic factors (rural electricity consumption, gross agricultural production, effective irrigation area, total power of agricultural machinery, consumption of agricultural plastic film, etc.) played a dominant role in rapeseed yield changes. The RF model had a good prediction effect on rapeseed yield, and natural factors and socio-economic factors had different effects on spring rapeseed and winter rapeseed. Winter rapeseed yield was mainly affected by socio-economic factors, accounting for as high as 89% of the importance. Among them, the sown area of rapeseed and the effective irrigation area had the greatest impact. The effects of natural factors and socio-economic factors on spring rapeseed yield were similar, accounting for 47% and 53%, respectively, and the mean annual precipitation, sunshine duration, and sown area of rapeseed were the most influential variables.

Estimation of the Total Soil Nitrogen Based on a Differential Evolution Algorithm from ZY1-02D Hyperspectral Satellite Imagery

Precise fertilizer application in agriculture requires accurate and dependable measurements of the soil total nitrogen (TN) concentration. Henan Province is one of the most important grain-producing areas in China. In order to promote the development of precision agriculture in Henan Province, this study took the high-standard basic farmland construction area in central Henan Province as the research area. Using single-phase images acquired from the ZY1-02D satellite hyperspectral sensor on 28 January 2021 (with a spatial resolution of 30 m × 30 m, a spectral range that covered 400–2500 nm, and a revisit period of 3 days) for spectral reflectance transformation and feature spectral band extraction. Based on multiple representation models, such as multiple linear regression, partial least squares regression, and support vector machine (SVM), all bands, feature bands, feature band combinations, and differential evolution (DE) algorithms were used to extract the secondary characteristic variables of the combination of characteristic bands, which were used as model inputs to estimate the content of TN in the study area. It was found that (1) the spectral reflectance transformation could help to improve the accuracy of prediction by reducing the interference from noise in the model, but the optimal spectral transformation method differed between different models and even between the training and test sets of the same model; (2) the estimation accuracy of the TN content model based on the minimum shrinkage and feature selection operator of the feature band was usually better than that of the full band, the feature combination band contained more effective information related to the TN content, and the combination of the DE algorithm and the SVM model achieved a better estimation accuracy for secondary feature extraction and TN content estimation of the feature combination band; and (3) ZY1-02D hyperspectral satellite data have the potential for the dynamic and non-destructive monitoring of regional TN content.

Demystifying the decadal shift in the extent of groundwater in the coastal aquifers of Gujarat, India: A case of reduced extent but increased magnitude of seawater intrusion

Catastrophic increase in urbanisation and industrialisation along the coastal region leads to increased stress on groundwater reservoirs worldwide. As a growing economy, India faces extreme water crises due to rising water demand and escalating salinisation, specifically in the coastal districts. Therefore, this study shows the implication of a comprehensive modelling approach to assess the spatiotemporal changes in hydrogeochemical processes in the coastal aquifer of the Surat district. Using a multi-model assessment approach, the present study focuses on the decadal evolution in groundwater quality of the coastal aquifers of Surat, Gujarat. Fifty-one groundwater samples were collected for 2008, 2012, and 2018 to assess the spatio-temporal shift in groundwater quality. Piper diagram revealed a shift of hydrogeochemical facies from Mg2+-HCO3− type to Ca2+-Mg2+-Cl− type, indicating the increased salinisation over a decade. The result suggests that rock-water interaction, seawater intrusion mechanism, and anthropogenic activities (intensive agricultural activities and improper waste management) govern the hydrogeochemical processes in the coastal aquifer. A shift of dominance of carbonate weathering to silicate weathering with the dissolution of calcite, dolomite, and gypsum, changing the hydrogeochemistry, was observed over the last decades. This shift leads to the increasing hardness of groundwater. The enrichment of nutrients in groundwater during 2018 (NO3− = 2 to 85 mg. L−1) compared to 2008 (NO3− = 1 to 36 mg.L−1) indicates the increasing imprints of agricultural fertilizer application and human organic waste through sewage contamination on the coastal aquifer. The seawater mixing index model demonstrates that extent of seawater intrusion reduced in 2018 compared to 2012, but the magnitude increased near the coastal talukas (SMI =9.5). The present study helps to understand the increasing anthropogenic activities over a decade leading to increased salinisation and groundwater contamination in the aquifer system. This work can help local stakeholders, water resource managers, and the state government manage the groundwater resources and the future potential threat of aquifer contamination.

Effect of biochar and bioorganic fertilizer on the microbial diversity in the rhizosphere soil of Sesbania cannabina in saline-alkaline soil.

Biochar and bioorganic fertilizer (BOF) application in agriculture has garnered increasing interest recently. However, the effects of biochar and BOF on rhizosphere soil microecology, especially in a region with saline-alkaline soil, remain largely unexplored. In this study, we performed Illumina-based 16S rRNA sequencing to investigate the effects of biochar with or without BOF addition, as well as at different addition rates and particles sizes, on the microecology of saline-alkaline rhizosphere soil. In the field experiment, biochar and BOF application altered the rhizosphere soil microecology. Actinobacteriota, Proteobacteria, and Chloroflexi accounted for >60% of the total bacterial population in each treatment. In the different treatments, Actinobacteria and Alphaproteobacteria were the predominant classes; Micromonosporales and Vicinamibacterales were the dominant orders; norank_f__Geminicoccaceae and Micromonosporaceae were the most abundant families; and Micromonospora and norank_f_Geminicoccaceae were the predominant genera. Application of biochar with or without BOF decreased soil electrical conductivity (EC) by 7% -11.58% only at the depth of 10 cm below the surface, again, soil EC can be significantly reduced by an average of 4% at 10 cm depth soil after planting Sesbania cannabina. Soil organic carbon, organic matter, available potassium, and available phosphorus contents had significant effects on the soil bacterial community structure. Co-application of biochar and BOF resulted in the greatest improvement of rhizosphere soil microecology, either by promoting plant growth or improving the nutrition and physicochemical properties of soil, followed by BOF alone and biochar alone. Additionally, higher application rate of biochar was better than lower application rate, and fine biochar had a stronger effect than coarse biochar. These results provide guidance for the development of new saline-alkaline soil remediation strategies.

A high-resolution inventory of ammonia emissions from agricultural fertilizer application and crop residue in Taiwan

The presence of ammonia (NH3) in the atmosphere has an impact on atmospheric nitrogen (N) deposition, air quality, and environmental systems. NH3 emissions in Taiwan are mainly driven by N fertilizer application and livestock operations, and their estimates are associated with considerable inaccuracies and uncertainties. In this study, a high-resolution (1 × 1 km) NH3 emissions inventory for agricultural fertilizers and crop residues in Taiwan was developed for 2016. The NH3 emissions were estimated based on detailed local activity data, such as seasonally varying planted areas and fertilizer application rates, and emissions from 88 different crops were resolved to better capture the seasonality and spatial heterogeneity. Emissions factors (EFs) are governed by ambient temperature, soil acidity and other factors. We further estimated NH3 emissions from two agricultural management scenarios, conventional farming (CF) and appropriate fertilization for crops (AF), to quantify the potential reduction in NH3 emissions due to government policy. The NH3 emissions from agricultural fertilizers and crop residues in Taiwan were approximately 25010 (20298.3) tons in 2016 for the CF (AF) scenario. Synthetic N fertilizer contributed 66.9% to the total emissions, followed by organic N fertilizer (32.1%), with crop residue (1.0%) being a relatively small source in the CF scenario. The NH3 emissions hotspots were located in the central to southern part of western Taiwan, where intense agricultural activities are conducted. The peak NH3 emissions occurred in spring and summer and were mainly associated with the cultivation of the first and second seasons of rice. We validated our estimated NH3 emissions with a satellite NH3 column, finding that the spatial-temporal variations in NH3 emissions from fertilizer application were in good coherency with the NH3 column concentration.

Carbon Accumulation Associated with Its Influencing Factors in Sediments from the Poyang Basin

Carbon burial in lake sediments has a profound impact on the global carbon cycle. In this study, the burial characteristics of organic carbon (OC) in typical sediments from the Poyang basin over the past hundred years were investigated and the influencing factors and driving mechanism were determined. The results showed that carbon burial in the Poyang basin sediments was mainly driven by human activities, whereas the change in the OC accumulation rate was related to precipitation, forest land area, water area, construction land area, urbanization rate, application of agricultural fertilizers, per capita GDP and population.

Groundwater Pollution Risk Assessment in Plain Area of the Yarkant River Basin

Groundwater pollution risk assessment is an effective method to manage groundwater resources and prevent groundwater pollution. The DRSTIW model was used to evaluate the groundwater vulnerability in a plain area of the Yarkant River Basin, and factor analysis was used to identify pollution sources for pollution loading evaluation. The functional value of groundwater was estimated by considering both the mining value and the in-situ value. The entropy weight method and the analytic hierarchy process (AHP) were used to determine the comprehensive weight, and a groundwater pollution risk map was generated based on the overlay function of ArcGIS software. The results showed that the natural geological factors such as large groundwater recharge modulus, wide recharge sources, strong permeability of soil surface and unsaturated zone, and shallow groundwater depth facilitated pollutant migration and enrichment, resulting in a higher overall groundwater vulnerability. Very high vulnerability and high vulnerability areas were mainly distributed in Zepu County, Shache County, Maigaiti County, Tumushuke City, and the eastern part of Bachu County. The pollution loading of groundwater was generally low, the main pollution sources were point source pollution caused by water-rock interaction, non-point source pollution caused by pesticides and fertilizers, and point source pollution caused by industry and life. The overall functional value of groundwater was low because of human economic activities, fine water quality, and good habitat quality. Groundwater pollution risk was generally low, and very high and high pollution risk areas accounted for 20.7% of the study area, mainly distributed in Shache County, Zepu County, Maigaiti County, Tumushuke City, and the western part of Bachu County. Natural conditions such as strong aquifer permeability, weak groundwater runoff conditions, large groundwater recharge modulus, low vegetation coverage, and strong water-rock interaction, coupled with frequent human activities such as application of agricultural fertilizers and discharge of industrial and domestic sewage, made the groundwater pollution risk higher in these areas. Groundwater pollution risk assessment provided strong data support for the optimization of the groundwater monitoring network and the prevention of groundwater pollution.

Soil and microbial C:N:P stoichiometries play vital roles in regulating P transformation in agricultural ecosystems: A review

Stoichiometry plays a crucial role in biogeochemical cycles and can modulate soil nutrient availability and functions. In agricultural ecosystems, phosphorus (P) fertilizers (organic or chemical) are often applied to achieve high crop yields. However, P is readily fixed by soil particles, leading to low P use efficiency. Therefore, understanding the role of carbon:nitrogen:P stoichiometries of soil and microorganisms in soil P transformation is of great significance for P management in agriculture. This paper provides a comprehensive review of the recent research on stoichiometry effect on soil P transformation in agricultural ecosystems. Soil microorganisms play an important role in the transformation of soil non-labile inorganic P to microbial biomass P by regulating microbial biomass stoichiometry. They also mobilize soil unavailable organic P into available P by changing ecoenzyme stoichiometry. Organic materials, such as manure and straw, play an important role in promoting the transformation of insoluble P into available P as well. Additionally, periphytic biofilms can reduce P loss from rice field ecosystems. Agricultural stoichiometries are different from those of natural ecosystems and thereby should receive more attention due to the influences of anthropogenic factors. Therefore, it is necessary to conduct further stoichiometry research on the soil biochemical mechanisms underlying P transformation in agricultural ecosystems. In conclusion, understanding stoichiometry impact on soil P transformation is crucial for P management in agricultural ecosystems.

STORET (Storage and Retrieval) method for analysis and identification of water pollutants in the Upper Citarum Watershed, West Java, Indonesia

Water pollution in the Citarum watershed is currently an urgent problem to control. Water pollution usually occurs due to the large number of pollutants entering the water bodies, such as from household, agricultural, industrial, livestock, and land use wastes. This study was conducted in the Citarum upstream watershed and divided into five locations: Situ Cisanti, Cikawao, Koyod, Rancamanyar, and Nanjung. This study aimed to analyze and identify the pollution level in the Citarum upstream watershed based on the BOD (biological oxygen demand), TDS (total dissolved solids), chloride, zinc, total detergent, nitrate, nitrite, lead, mercury, and phenol concentrations. These parameter data were then compared to the Indonesian government regulation for river water quality standards No. 22/2021 and the STORET (Storage and Retrieval) method, based on the United States Environmental Protection Agency (US EPA). Water sampling from each location was carried out in different seasons. The STORET value in the rainy season was categorized as slightly polluted with the value of -10, then became -34 in the dry season as heavily polluted. Therefore, water pollution management is necessary to sustain the water resources by controlling the domestic waste and fertilizer application in agriculture and livestock culture.

Biochar enhancement of nitrification processes varies with soil conditions

Application of inorganic nitrogen (N) fertilizers in agriculture can increase emissions of nitrous oxide, a potent greenhouse gas, leaching of nitrate (NO3−), a groundwater contaminant hazardous to human health, and soil acidification. Soil amendment with biochar potentially mitigates these losses and undesirable outcomes. However, there have been considerable inconsistencies in reported impacts, likely owing to variable physiochemical characteristics of the biochar materials and/or the soil environment. This study methodically evaluated the impact of biochar soil incorporation on N transformation and underlying microbial processes using soils with varying biochar types, soil texture, soil moisture, and manure compost co- amendments. Laboratory incubations were conducted to monitor the fate of urea fertilizer N spiked in biochar amended and unamended soils by assaying soil ammonium (NH4+), nitrite (NO2−), and NO3− concentrations, pH, and abundances of soil nitrifiers; ammonia oxidizing bacteria and archaea (AOB and AOA) and Nitrospira with the capacity to perform complete ammonia oxidation (comammox). Soil moisture was a critical factor affecting N transformation processes, more so than biochar, but biochar did result in significantly different concentrations of N species in response to urea application. Biochar enhanced nitrification, more significantly in drier conditions and in sandy soil. Biochar offered some buffering potential in the neutral-alkaline, unsaturated soils, preventing >1 unit drop in pH compared to unamended soils. Co-application of biochar with manure composts enhanced nitrification slightly, which was evidenced by higher abundances of some soil nitrifiers at 4 weeks, although increases in nitrification rates were not statistically significant. Soil nitrifier populations tended to increase in response to a pinewood biochar, but trends differed for saturated soil, in soils of differing textures, or when different biochar materials were evaluated. Thus, when evaluating implications of biochar on the fate of mineral N fertilizer, soil moisture and other environment conditions should be considered.

Real-time detection of ammonium in soil pore water

The development of technologies for continuous measurement of nitrogen forms in the soil is essential for optimizing the application of fertilizers in agriculture and preventing water-resource pollution. However, there is no effective commercial technology available for continuous monitoring of ammonium species in soil pore water. This work investigates an approach for real-time measurement of ammonium in soil water using near-infrared transmission spectroscopy and partial least squares regression (PLSR) for spectral analysis. The PLSR model was trained using soil pore water collected from various soils spiked with ammonium to achieve a wide concentration range. The monitoring approach was then validated through transport experiments in a soil column. The results demonstrated capabilities for real-time tracking of the temporal variation in soil ammonium concentration and potential utilization in agronomical or environmental sensing.

A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery

The accurate and rapid estimation of canopy nitrogen content (CNC) in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture. However, the determination of CNC from field sampling data for leaf area index (LAI), canopy photosynthetic pigments (CPP; including chlorophyll a, chlorophyll b and carotenoids) and leaf nitrogen concentration (LNC) can be time-consuming and costly. Here we evaluated the use of high-precision unmanned aerial vehicle (UAV) multispectral imagery for estimating the LAI, CPP and CNC of winter wheat over the whole growth period. A total of 23 spectral features (SFs; five original spectrum bands, 17 vegetation indices and the gray scale of the RGB image) and eight texture features (TFs; contrast, entropy, variance, mean, homogeneity, dissimilarity, second moment, and correlation) were selected as inputs for the models. Six machine learning methods, i.e., multiple stepwise regression (MSR), support vector regression (SVR), gradient boosting decision tree (GBDT), Gaussian process regression (GPR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), were compared for the retrieval of winter wheat LAI, CPP and CNC values, and a double-layer model was proposed for estimating CNC based on LAI and CPP. The results showed that the inversion of winter wheat LAI, CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs. The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI, CPP and CNC. The proposed double-layer models (R2=0.67–0.89, RMSE=13.63–23.71 mg g–1, MAE=10.75–17.59 mg g–1) performed better than the direct inversion models (R2=0.61–0.80, RMSE=18.01–25.12 mg g–1, MAE=12.96–18.88 mg g–1) in estimating winter wheat CNC. The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs (R2=0.89, RMSE=13.63 mg g–1, MAE=10.75 mg g–1). The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.

The tempo-spatial changes of soil fertility in farmland of China from the 1980s to the 2010s

The grain yield level is closely related to soil fertility. To clarify the status and variations of soil fertility in Chinese farmland, soil sample profile data from the Soil Species of China in the 1980s and the Soil Series of China in the 2010s were collected. And surface soil data of fertility indexes (pH, soil organic matter, total nitrogen, total phosphorus, and total potassium) were calculated. The tempo-spatial variations characteristics of both soil fertility indexes and soil comprehensive fertility of Chinese farmland were analyzed with descriptive statistics, comparative analysis of paired sample points, and Integrated Fertility Index method. The results showed that: 1) The farmland soils in southern China were acidic and those in northern were alkaline. From the 1980s to the 2010s, the pH mean decreased by 0.34, and the acidification of soils in the southern region was more significant; 2) SOM and TN contents showed similar spatial patterns, with higher contents in southern and northeastern regions. Compared with the 1980s, the SOM mean and TN mean in the 2010s increased by 2.72 g/kg and 0.06 g/kg, respectively, with a decreasing trend in some areas; 3) The spatial distribution of TP and TK contents showed significant variability, with change range of the mean values was 0.15 g/kg and −1.51 g/kg during the three decades, respectively; 4) In general, the soil fertility in Chinese farmland was poor, with the most sample points at Grade IV (Mid-low) in the 2010s (22.38 %). During the three decades, the IFI mean increased by 0.01. The number of sample points with increasing IFI in eastern China was more than that in western China. And the soil fertility in eastern China showed a trend of ameliorating. This study would have great scientific significance for guiding agricultural fertilizer application and improving fertility in farmland soils.

Activity and Selectivity of Bimetallic Catalysts Based on SBA-15 for Nitrate Reduction in Water

Nitrate from the application of nitrogen-based fertilizers in intensive agriculture is a notorious waste product, though it lacks cost-effective solutions for its removal from potential drinking water resources. Catalytic reduction appears to be a promising technique for converting nitrates to benign nitrogen gas. Mesoporous silica SBA-15 is a frequently used catalyst support that has large surface areas and highly ordered nanopores. In this work, mesoporous silica SBA-15 bimetallic catalysts for nitrate reduction were investigated. The catalyst was optimized for the selection of promoter metal (Sn and Cu), noble metal (Pd and Pt) and loading ratios of these metals at different temperatures and reduction conditions. The catalysts prepared were characterized by FT-IR, N2 physisorption, XRD, SEM, and ICP. All catalysts showed the presence of cylindrical mesoporous channels and uniform pore structures that remained even after metals loading. In the presence of a CO2 buffer, the catalysts 4Pd-1Cu/SBA-15 and 1Pt-1Cu/SBA-15 reduced at 100?C under H2 and 1Pd-1Cu/SBA-15 reduced at 200°C under H2 demonstrated very high nitrate conversion. Furthermore, the forementioned Pd catalysts had higher N2 selectivity (88% - 87%) compared to Pt catalyst (80%). Nitrate conversion by the 4Pd-1Cu/SBA-15 catalyst was significantly decreased to 81% in the absence of CO2.

Role of phosphorus in the seasonal deoxygenation of the East China Sea shelf

Abstract. The Changjiang is the largest river in Asia and the main terrestrial source of freshwater and nutrients to the East China Sea (ECS). Nutrient concentrations have long been increasing in the Changjiang, especially after 1960 with urbanization, the development of industrial animal production, and fertilizer application in agriculture, resulting in coastal eutrophication and recurring summer hypoxia. The supply of anthropogenic nitrogen (N) exceeds that of phosphorus (P) relative to the Redfield ratio. This results in seasonal P limitation in the Changjiang plume. P limitation and its effects on primary production, respiration, and hypoxia in the ECS have not been studied systematically, although such knowledge is needed to understand bloom dynamics in the region, to assess the consequences of altered nutrient loads, and to implement nutrient reduction strategies that mitigate hypoxia. Using a coupled physical–biogeochemical model of the ECS that was run with and without P limitation, we quantify the distribution and effects of P limitation. The model shows that P limitation develops eastward of the Changjiang Estuary and on the Yangtze Bank but rarely southward along the Zhejiang coast. P limitation modifies oxygen sinks over a large area of the shelf by partly relocating primary production and respiration offshore, away from the locations prone to hypoxia near the Changjiang Estuary. This relocation drastically reduces sediment oxygen consumption nearshore and dilutes the riverine-driven primary production and respiration over a large area offshore. Our results suggest that the hypoxic zone would be 48 % larger in its horizontal extent, on average, if P limitation was not occurring. Results are summarized in a conceptual model of P limitation on the ECS shelf that is also applicable to other systems. Then we carried out nutrient reduction simulations which indicate that, despite the effect of P limitation on hypoxia, reducing only P inputs as a nutrient reduction strategy would not be effective. A dual N + P nutrient reduction strategy would best mitigate hypoxia. The model results suggest that decreasing the size of the hypoxic zone by 50 % and 80 % would require reductions in N + P load of 28 % and 44 %, respectively.

Stable Isotope Analysis of Greenhouse Gases Requires Analyte Preconcentration

Nitrous oxide (N2O) is an important trace gas contributing to global warming and depletion of ozone in the stratosphere. Its increasing abundance is caused mainly by anthropogenic sources, such as application of fertilizers in agriculture or emissions from industry. To understand the N2O global budget, its sources and sinks need to be well-described and quantified. In this project, a new method for N2O source appointment was developed that can help with this task. The method is based on analysis of the eight most abundant isotopic molecules of N2O, using quantum cascade laser absorption spectroscopy (QCLAS). The applicability of the method towards the N2O biogeochemical cycle was demonstrated on a prominent N2O source (bacterial denitrification) and the most important N2O sink (UV photolysis) on samples prepared in laboratory experiments. An extension of the QCLAS method to natural samples can be achieved by hyphenation with a preconcentration technique that increases concentration of the analyte and standardizes the sample matrix. This article provides an overview of currently applied preconcentration techniques in the field of greenhouse-gas analysis and a description of the preconcentration device TREX that will be employed in future projects with the developed QCLAS method.

Silicon as a Smart Fertilizer for Sustainability and Crop Improvement

Silicon (Si), despite being abundant in nature, is still not considered a necessary element for plants. Si supplementation in plants has been extensively studied over the last two decades, and the role of Si in alleviating biotic and abiotic stress has been well documented. Owing to the noncorrosive nature and sustainability of elemental Si, Si fertilization in agricultural practices has gained more attention. In this review, we provide an overview of different smart fertilizer types, application of Si fertilizers in agriculture, availability of Si fertilizers, and experiments conducted in greenhouses, growth chambers, and open fields. We also discuss the prospects of promoting Si as a smart fertilizer among farmers and the research community for sustainable agriculture and yield improvement. Literature review and empirical studies have suggested that the application of Si-based fertilizers is expected to increase in the future. With the potential of nanotechnology, new nanoSi (NSi) fertilizer applications may further increase the use and efficiency of Si fertilizers. However, the general awareness and scientific investigation of NSi need to be thoughtfully considered. Thus, we believe this review can provide insight for further research into Si fertilizers as well as promote Si as a smart fertilizer for sustainability and crop improvement.