Nadhir Al-Ansari

Nadhir Al-Ansari

Luleå tekniska Universitet

H-index: 65

Europe-Sweden

About Nadhir Al-Ansari

Nadhir Al-Ansari, With an exceptional h-index of 65 and a recent h-index of 62 (since 2020), a distinguished researcher at Luleå tekniska Universitet, specializes in the field of Water resources, Environment, Geology, Civil Engineering.

His recent articles reflect a diverse array of research interests and contributions to the field:

Long-term application of agronomic management strategies effects on soil organic carbon, energy budgeting, and carbon footprint under rice–wheat cropping system

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique

Hybrid river stage forecasting based on machine learning with empirical mode decomposition

An Analytical Surveillance of Land Utilization and Expansion of Urban Areas Employing Remote Sensing Algorithms

Evaluation of the impacts of seawater integration to electrocoagulation for the removal of pollutants from textile wastewater

Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

Publisher Correction: Long‑term application of agronomic management strategies effects on soil organic carbon, energy budgeting, and carbon footprint under rice–wheat cropping …

A simplistic approach for monitoring meteorological drought over arid regions: a case study of Rajasthan, India

Nadhir Al-Ansari Information

University

Luleå tekniska Universitet

Position

___

Citations(all)

17508

Citations(since 2020)

15406

Cited By

5270

hIndex(all)

65

hIndex(since 2020)

62

i10Index(all)

411

i10Index(since 2020)

355

Email

University Profile Page

Luleå tekniska Universitet

Nadhir Al-Ansari Skills & Research Interests

Water resources

Environment

Geology

Civil Engineering

Top articles of Nadhir Al-Ansari

Long-term application of agronomic management strategies effects on soil organic carbon, energy budgeting, and carbon footprint under rice–wheat cropping system

Authors

RK Naresh,PK Singh,Rajan Bhatt,Mandapelli Sharath Chandra,Yogesh Kumar,NC Mahajan,SK Gupta,Nadhir Al-Ansari,Mohamed A Mattar

Journal

Scientific Reports

Published Date

2024/1/3

In the plains of western North India, traditional rice and wheat cropping systems (RWCS) consume a significant amount of energy and carbon. In order to assess the long-term energy budgets, ecological footprint, and greenhouse gas (GHG) pollutants from RWCS with residual management techniques, field research was conducted which consisted of fourteen treatments that combined various tillage techniques, fertilization methods, and whether or not straw return was present in randomized block design. By altering the formation of aggregates and the distribution of carbon within them, tillage techniques can affect the dynamics of organic carbon in soil and soil microbial activity. The stability of large macro-aggregates (> 2 mm), small macro-aggregates (2.0–2.25 mm), and micro-aggregates in the topsoil were improved by 35.18%, 33.52%, and 25.10%, respectively, over conventional tillage (0–20 cm) using tillage …

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique

Authors

Sidhartha Sekhar Swain,Tapan Kumar Khura,Pramod Kumar Sahoo,Kapil Atmaram Chobhe,Nadhir Al-Ansari,Hari Lal Kushwaha,Nand Lal Kushwaha,Kanhu Charan Panda,Satish Devram Lande,Chandu Singh

Journal

Scientific Reports

Published Date

2024/2/6

An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and groundwater pollution reduction. However, past studies could not efficiently model nitrate leaching due to utilisation of conventional algorithms. To address the issue, the current research employed advanced machine learning algorithms, viz., Support Vector Machine, Artificial Neural Network, Random Forest, M5 Tree (M5P), Reduced Error Pruning Tree (REPTree) and Response Surface Methodology (RSM) to predict and optimize nitrate leaching. In this study, Urea Super Granules (USG) with three different coatings were used for the experiment in the soil columns, containing 1 kg soil with fertiliser placed in between. Statistical parameters, namely correlation coefficient, Mean Absolute Error, Willmott index, Root Mean Square Error and Nash–Sutcliffe efficiency were used to evaluate the performance of the ML techniques. In …

Hybrid river stage forecasting based on machine learning with empirical mode decomposition

Authors

Salim Heddam,Dinesh Kumar Vishwakarma,Salwan Ali Abed,Pankaj Sharma,Nadhir Al-Ansari,Abed Alataway,Ahmed Z Dewidar,Mohamed A Mattar

Journal

Applied Water Science

Published Date

2024/3

The river stage is certainly an important indicator of how the water level fluctuates overtime. Continuous control of the water stage can help build an early warning indicator of floods along rivers and streams. Hence, forecasting river stages up to several days in advance is very important and constitutes a challenging task. Over the past few decades, the use of machine learning paradigm to investigate complex hydrological systems has gained significant importance, and forecasting river stage is one of the promising areas of investigations. Traditional in situ measurements, which are sometime restricted by the existing of several handicaps especially in terms of regular access to any points alongside the streams and rivers, can be overpassed by the use of modeling approaches. For more accurate forecasting of river stages, we suggest a new modeling framework based on machine learning. A hybrid forecasting …

An Analytical Surveillance of Land Utilization and Expansion of Urban Areas Employing Remote Sensing Algorithms

Authors

Nadhir Al-Ansari,Hayder Dibs,Ahmed Al-Janabi

Published Date

2024/4/8

In recent years, the city of AL-Hilla in Babylon, Iraq has suffered from the illegal fragmentation of agricultural and orchard lands, leading to their conversion into residential areas. This transformation has had a negative impact on the economic viability of plantation and vegetation lands, affecting the climate and causing an increase in temperatures, winds, and dust storms. This study aims to examine the spatio-temporal dynamics of changes in land-use/land-cover (LU/LC) using different spatial resolutions of satellite images to detect urban sprawl. The present study utilizes a supervised imagery classifier, employing the Mahalanobis distance (MD) technique to produce three distinct LU/LC maps for 2002, 2011, and 2022. The accuracy of the outcomes is assessed using a confusion matrix, and a comparison was made to compute the changes in land categories. The research reveals that the expansion of the urban region in AL-Hilla has significantly increased from 33.40 km² in 2002 to 89.16 km² in 2022, with an Annual Growth Rate of (6.74%) between 2002 and 2011 and 6.14% between 2011 and 2022. The growth in urban area now constitutes 38.45% of the entire city area and has resulted in a decline in other land categories such as water bodies, soil, and vegetation. The study highlights the necessity for effective management and planning strategies to address the adverse impact of urban expansion on the environment and agriculture

Evaluation of the impacts of seawater integration to electrocoagulation for the removal of pollutants from textile wastewater

Authors

Tahmeed Ahmed,Md Habibur Rahman Bejoy Khan,Amimul Ahsan,Nafis Islam,Moetaz El-Sergany,Md Shafiquzzaman,Monzur Imteaz,Nadhir Al-Ansari

Journal

Environmental Sciences Europe

Published Date

2024/4/16

Recent textile industry expansion has a major environmental impact if not addressed. Being a water intensive industry, textile manufacturing is usually associated with wastewater management challenges. Electrocoagulation (EC) is recognized as one of the effective solutions to address these challenges. This study aims to investigate the potential of integrating seawater into the EC process for textile wastewater treatment, targeting optimal pollutant removal efficiencies. A simple electrolytic reactor was designed to investigate the removal efficiency of these treatments for chemical oxygen demand (COD), total suspended solids (TSS), turbidity, and color from textile wastewater at different seawater percentages and retention times. Notably, the addition of seawater not only improves the EC process efficiency but also significantly dilutes pollutants, reducing their concentrations. This dual effect enhances removal …

Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

Authors

Baydaa Abdul Kareem,Salah L Zubaidi,Nadhir Al-Ansari,Yousif Raad Muhsen

Journal

CMES-Computer Modeling in Engineering & Sciences

Published Date

2024

Forecasting river flow is crucial for optimal planning, management, and sustainability using freshwater resources. Many machine learning (ML) approaches have been enhanced to improve streamflow prediction. Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches. Current researchers have also emphasised using hybrid models to improve forecast accuracy. Accordingly, this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years, summarising data preprocessing, univariate machine learning modelling strategy, advantages and disadvantages of standalone ML techniques, hybrid models, and performance metrics. This study focuses on two types of hybrid models: parameter optimisation-based hybrid models (OBH) and hybridisation of parameter optimisation-based and preprocessing-based hybrid models (HOPH). Overall, this research supports the idea that meta-heuristic approaches precisely improve ML techniques. It’s also one of the first efforts to comprehensively examine the efficiency of various meta-heuristic approaches (classified into four primary classes) hybridised with ML techniques. This study revealed that previous research applied swarm, evolutionary, physics, and hybrid metaheuristics with 77%, 61%, 12%, and 12%, respectively. Finally, there is still room for improving OBH and HOPH models by examining different data pre-processing techniques and metaheuristic algorithms.

Publisher Correction: Long‑term application of agronomic management strategies effects on soil organic carbon, energy budgeting, and carbon footprint under rice–wheat cropping …

Authors

RK Naresh,PK Singh,Rajan Bhatt,Mandapelli Sharath Chandra,Yogesh Kumar,NC Mahajan,SK Gupta,Nadhir Al-Ansari,Mohamed A Mattar

Journal

Scientific Reports

Published Date

2024

Publisher Correction: Long‑term application of agronomic management strategies effects on soil organic carbon, energy budgeting, and carbon footprint under rice–wheat cropping system - PMC Back to Top Skip to main content NIH NLM Logo Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now. Search PMC Full-Text Archive Search in PMC Advanced Search User Guide Journal List Scientific Reports PMC10942967 Other Formats PDF (661K) Actions Cite Collections Share Permalink Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Journal List Scientific Reports PMC10942967 As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National …

A simplistic approach for monitoring meteorological drought over arid regions: a case study of Rajasthan, India

Authors

Sabyasachi Swain,Prabhash Kumar Mishra,Saswata Nandi,Biswajeet Pradhan,Sashikanta Sahoo,Nadhir Al-Ansari

Journal

Applied Water Science

Published Date

2024/2

The commonly used precipitation-based drought indices typically rely on probability distribution functions that can be suitable when the data exhibit minimal discrepancies. However, in arid and semi-arid regions, the precipitation data often display significant discrepancies due to highly irregular rainfall patterns. Consequently, imposing any probability distributions on the data for drought analysis in such regions may not be effective. To address this issue, this study employs a novel drought index called the Discrepancy Precipitation Index (DPI), specifically designed for arid regions. Unlike traditional methods, the DPI does not impose a probability distribution on the precipitation data; instead, it relies on the discrepancy between the data and the mean value. Drought severity classifications (i.e., Drought-I, Drought-II, and Drought-III) are proposed based on the DPI values. The DPI is used to characterize and assess …

Heatwaves in Peninsular Malaysia: a spatiotemporal analysis

Authors

Mohd Khairul Idlan Muhammad,Mohammed Magdy Hamed,Sobri Harun,Zulfaqar Sa’adi,Saad Sh Sammen,Nadhir Al-Ansari,Shamsuddin Shahid,Miklas Scholz

Journal

Scientific Reports

Published Date

2024/2/21

One of the direct and unavoidable consequences of global warming-induced rising temperatures is the more recurrent and severe heatwaves. In recent years, even countries like Malaysia seldom had some mild to severe heatwaves. As the Earth's average temperature continues to rise, heatwaves in Malaysia will undoubtedly worsen in the future. It is crucial to characterize and monitor heat events across time to effectively prepare for and implement preventative actions to lessen heatwave's social and economic effects. This study proposes heatwave-related indices that take into account both daily maximum (Tmax) and daily lowest (Tmin) temperatures to evaluate shifts in heatwave features in Peninsular Malaysia (PM). Daily ERA5 temperature dataset with a geographical resolution of 0.25° for the period 1950–2022 was used to analyze the changes in the frequency and severity of heat waves across PM, while the …

Estimation of Potato Water Footprint Using Machine Learning Algorithm Models in Arid Regions

Authors

Amal Mohamed,Mohamed Abuarab,Nadhir Al-Ansari,Hazem Sayed,Mohamed A Kassem,Ahmed Elbeltagi,Ali Mokhtar

Published Date

2023/1/12

Precise assessment of water footprint to enhance water consumption and crop yields for irrigated agricultural efficiency is required in order to achieve water management sustainability. Although Penman-Monteith is more successful than others and is the most frequently used technique to calculate water footprint it requires a significant number of meteorological parameters at different spatio-temporal scales, sometimes inaccessible in many of the poor nations. Due to the greatest performance in the non-linear relations of inputs and output of the model, the complex hydrological phenomena are frequently described in machine learning models. Therefore, the objective of this research is to 1) develop and compare between the four-machine learning: Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boost (XGB) and Artificial Neural Network (ANN) over three potato’s governorates (Al-Gharbia, Al-Dakahlia, and Al-Beheira) in Delta, Egypt and 2) select the best model in the best combination of climate input variables, which achieves high precision and low error in forecasting potato blue WF. The available variables for this study are maximum temperature (T max), minimum temperature (T min), average temperature (T ave), wind speed (WS), relative humidity (RH), precipitation (P), vapor pressure deficit (VPD), solar radiation (SR), Sown area (SA), and crop coefficient (Kc) to predict potato BWFP during (1990–2016). Six scenarios of input variables were used to test the weight of each variable in for four applied models. Different statistical indicators have been used to assess applied model performance (NSE, RMSE, MAE …

Solar Radiation Prediction in Adrar, Algeria: A Case Study of Hybrid Extreme Machine-Based Techniques

Authors

Mohammed Benatallah,Nadjem Bailek,Kada Bouchouicha,Alireza Sharifi,Yasser Abdel-Hadi,Samuel C Nwokolo,Nadhir Al-Ansari,Ilhami Colak,Laith Abualigah,El-Sayed M. El-kenawy

Journal

International Journal of Engineering Research in Africa

Published Date

2024/4/15

This study delves into the application of hybrid extreme machine-based techniques for solar radiation prediction in Adrar, Algeria. The models under evaluation include the Extreme Learning Machine (ELM), Weighted Extreme Learning Machine (WELM), and Self-Adaptive Extreme Learning Machine (SA-ELM), with a comparative analysis based on various performance metrics. The results show that SA-ELM achieves the highest accuracy with an R2 of 0.97, outperforming ELM and WELM by 4.6% and 15.4% respectively in terms of R2. SA-ELM also has the lowest MPE, RMSE and RRMSE values, indicating a higher accuracy in predicting global radiation. Furthermore, comparison with previously employed prediction techniques solidifies SA-ELM’s superiority, evident in its 0.275 RMSE.The study explores different input combinations for predicting global radiation in the study region, concluding that incorporating all …

Publisher Correction: Isotherms, kinetics and thermodynamic mechanism of methylene blue dye adsorption on synthesized activated carbon

Authors

Atef El Jery,Heba Saed Kariem Alawamleh,Mustafa Humam Sami,Hussein Abdullah Abbas,Saad Sh Sammen,Amimul Ahsan,MA Imteaz,Abdallah Shanableh,Md Shafiquzzaman,Haitham Osman,Nadhir Al-Ansari

Journal

Scientific Reports

Published Date

2024

In the original version of this Article, Amimul Ahsan was omitted as a co-corresponding author. The corresponding authors for this Article are Amimul Ahsan and Nadhir Al-Ansari. Correspondence and request for materials should be addressed to ahsan. iut2@ gmail. com and nadhir. alansari@ ltu. se.

Groundwater delineation for sustainable improvement and development aided by GIS, AHP, and MIF techniques

Authors

Muhsan Ehsan,Haider Shabbir,Ayad M Fadhil Al-Quraishi,Nadhir Al-Ansari,Zulfiqar Ahmad,Kamal Abdelrahman,Muhammad Tayyab Sohail,Zaira Manzoor,Ahsan Shafi,Ahmed Elbeltagi

Journal

Applied Water Science

Published Date

2024/2

Exploration of groundwater is an integral part of viable resource growth for society, economy, and irrigation. However, uncontrolled utilization is mainly reported in urban and industries due to the increasing demand for water in semi-arid and arid regions of the world. In the background, groundwater demarcation for potential areas is vital in meeting necessary demand. The current study applied an integrated method comprising the analytical hierarchy process (AHP), multiple influence factors (MIF), combined with a linear regression curve and observatory well data for groundwater prospects mapping. Thematic maps such as flow direction, flow accumulation, elevation map, land use land cover, slope, soil texture, hill shade, geomorphology, normalized vegetation index, and groundwater depth map were generated utilizing remote sensing techniques. The relative weight of each parameter was estimated and then …

Comparison activity of pure and chromium-doped nickel oxide nanoparticles for the selective removal of dyes from water

Authors

Zahraa H Athab,Ahmed F Halbus,Sura Bahaa Mohammed,Abbas J Atiyah,Hussein Idrees Ismael,Nahlah Salman Saddam,Sadiq J Baqir,Hasan F Alesary,Sameer Algburi,Nadhir Al-Ansari

Journal

Scientific Reports

Published Date

2024/2/18

The current study involves a synthesis of a composite of nickel oxide nanoparticles (NiONPs) with a chromium dopant to yield (Cr/NiONPs). Synthesis of nickel oxide was performed by the co-precipitation method. The synthesis of the composite was conducted by the impregnation method. FTIR, EDX, SEM, and XRD were used to characterize the synthesized materials. The synthesised materials’ point zero charges (PZC) were performed using the potentiometric titration method. The obtained results show that the PZC for neat nickel oxide was around 5, and it was around 8 for Cr/NiONPs. The adsorption action of the prepared materials was examined by applying them to remove Reactive Red 2 (RR2) and Crystal Violate (CV) dyes from solutions. The outcomes demonstrated that Cr/NiONPs were stronger in the removal of dyes than NiONPs. Cr/NiONPs achieved 99.9% removal of dyes after 1 h. Adsorption isotherms …

Calibration, validation and uncertainty analysis of a SWAT water quality model

Authors

Sushil K Das,Amimul Ahsan,Md Habibur Rahman Bejoy Khan,Abdullah Gokhan Yilmaz,Shakil Ahmed,Monzur Imteaz,Muhammad Atiq Ur Rehman Tariq,Md Shafiquzzaman,Anne WM Ng,Nadhir Al-Ansari

Journal

Applied Water Science

Published Date

2024/4

Sediment and nutrient pollution in water bodies is threatening human health and the ecosystem, due to rapid land use changes and improper agricultural practices. The impact of the nonpoint source pollution needs to be evaluated for the sustainable use of water resources. An ideal tool like the soil and water assessment tool (SWAT) can assess the impact of pollutant loads on the drainage area, which could be beneficial for developing a water quality management model. This study aims to evaluate the SWAT model’s multi-objective and multivariable calibration, validation, and uncertainty analysis at three different sites of the Yarra River drainage area in Victoria, Australia. The drainage area is split into 51 subdrainage areas in the SWAT model. The model is calibrated and validated for streamflow from 1990 to 2008 and sediment and nutrients from 1998 to 2008. The results show that most of the monthly and …

Evaluate effect of 126 pre-processing methods on various artificial intelligence models accuracy versus normal mode to predict groundwater level (case study: Hamedan-Bahar …

Authors

Mohsen Saroughi,Ehsan Mirzania,Mohammed Achite,Okan Mert Katipoğlu,Nadhir Al-Ansari,Dinesh Kumar Vishwakarma,Il-Moon Chung,Maha Awjan Alreshidi,Krishna Kumar Yadav

Journal

Heliyon

Published Date

2024/4/15

The estimation of groundwater levels is crucial and an important step in ensuring sustainable management of water resources. In this paper, selected piezometers of the Hamedan-Bahar plain located in west of Iran. The main objective of this study is to compare effect of various pre-processing methods on input data for different artificial intelligence (AI) models to predict groundwater levels (GWLs). The observed GWL, evaporation, precipitation, and temperature were used as input variables in the AI algorithms. Firstly, 126 method of data pre-processing was done by python programming which are classified into three classes: 1- statistical methods, 2- wavelet transform methods and 3- decomposition methods; later, various pre-processed data used by four types of widely used AI models with different kernels, which includes: Support Vector Machine (SVR), Artificial Neural Network (ANN), Long-Short Term memory …

Detention and Release in Stepped Gabion Weir: Case of Four Steps

Authors

Ali Mekki Al-Fawzy,Ahmed Mohammed Sami Al-Janabi,Walid Djamaa,Nadhir Abbas Al-Ansari,Riyadh Jasim Al-Saadi

Published Date

2024

The problem of water scarcity can be noticed clearly in the lined canals which provide the irrigation networks. Using porous structures like gabion weirs contributes as a part solution to this problem. In the current study, a laboratory flume was used to calculate the water depths upstream and downstream of the stepped gabion weir that is to be put inside it at a certain distance, and this flume comes with dimensions of 10 m long by 0.30 m wide and 0.50 m height. While the tested hydraulic model of the weir was built with dimensions of 0.30 m width by 0.40 m maximum height, and five lengths with different total distance of 0.88, 0.96, 1.08, 1.12, and 1.20 m respectively. The used gravel samples to fill the gabions were of monosize query gravel with diameters ranging between 0.0095-0.0140, 0.0140-0.0190, 0.0190-0.0250, 0.0250-0.0375, and 0.0375-0.0500 m in a respective way. While the values of discharge, measured during the experiments were in the range of 0.0007-0.0150 m3/s, and a total of 175 trial tests. This study achieved that the detention depth value decreases by increasing the diameter of the gravel sample used, but there is no effect of the gravel sample on the value of release depth, the different illustrated formulas for the detention and release depths maybe can be used usefully for design and scheduling actions in the field where it gave a reasonable matching between the measured and the calculated values of the studied depths, and finally, the errors percentage in an average value for both detention and release tested values were 5.278% and-0.265% respectively

Interactive effects of long-term management of crop residue and phosphorus fertilization on wheat productivity and soil health in the rice–wheat

Authors

Rajeev Kumar Gupta,Paramjit Kaur Sraw,Jasjit Singh Kang,Jagroop Kaur,Vivek Sharma,Neemisha Pathania,Anu Kalia,Nadhir Al-Ansari,Abed Alataway,Ahmed Z Dewidar,Mohamed A Mattar

Journal

Scientific Reports

Published Date

2024/1/16

In the context of degradation of soil health, environmental pollution, and yield stagnation in the rice–wheat system in the Indo-Gangetic Plains of South Asia, an experiment was established in split plot design to assess the long-term effect of crop residue management on productivity and phosphorus requirement of wheat in rice–wheat system. The experiment comprised of six crop residue management practices as the main treatment factor with three levels (0, 30 and 60 kg P2O5 ha–1) of phosphorus fertilizer as sub-treatments. Significant improvement in soil aggregation, bulk density, and infiltration rate was observed under residue management (retention/incorporation) treatments compared to residue removal or residue burning. Soil organic carbon (SOC), available nutrient content (N, P, and K), microbial count, and enzyme activities were also significantly higher in conservation tillage and residue-treated plots …

Evaluate sediment transport formulas in the Euphrates river upstream Ramadi barrage in the west of Iraq

Authors

Sadeq Oleiwi Sulaiman,Abu Baker Ahmed Najm,Mohammad Falah Allawi,Nadhir Al Ansari,Ammar Hatem Kamel

Journal

AIP Conference Proceedings

Published Date

2024/2/14

The sediment problem in rivers is one of the common problems faced by most hydraulic establishments such as dams, water treatment plants, and other facilities. It is necessary to measure and evaluate the quantity of sediment for bedload and suspended load through the water stream before establishing any facility. There are several formulas used to calculate sediment transport around the world. The validity of these formulas has been verified in several regions. However, the conditions for applying these formulas differ from one region to another, and the accuracy of using one of these formulas in one region may be better than using it in another region. This study aims to evaluate these formulas used to calculate the sediment transport in the Euphrates River upstream of Ramadi Barrage in the west of Iraq. The field data were used to evaluate the different sediment transport equations and compare the calculated …

Biochar influences nitrogen and phosphorus dynamics in two texturally different soils

Authors

Rajeev Kumar Gupta,Monika Vashisht,RK Naresh,Nitish Dhingra,Mehra S Sidhu,PK Singh,Neeraj Rani,Nadhir Al-Ansari,Abed Alataway,Ahmed Z Dewidar,Mohamed A Mattar

Journal

Scientific Reports

Published Date

2024/3/19

Nitrogen (N) and phosphorus (P) are vital for crop growth. However, most agricultural systems have limited inherent ability to supply N and P to crops. Biochars (BCs) are strongly advocated in agrosystems and are known to improve the availability of N and P in crops through different chemical transformations. Herein, a soil-biochar incubation experiment was carried out to investigate the transformations of N and P in two different textured soils, namely clay loam and loamy sand, on mixing with rice straw biochar (RSB) and acacia wood biochar (ACB) at each level (0, 0.5, and 1.0% w/w). Ammonium N (NH4-N) decreased continuously with the increasing incubation period. The ammonium N content disappeared rapidly in both the soils incubated with biochars compared to the unamended soil. RSB increased the nitrate N (NO3–N) content significantly compared to ACB for the entire study period in both texturally …

See List of Professors in Nadhir Al-Ansari University(Luleå tekniska Universitet)

Nadhir Al-Ansari FAQs

What is Nadhir Al-Ansari's h-index at Luleå tekniska Universitet?

The h-index of Nadhir Al-Ansari has been 62 since 2020 and 65 in total.

What are Nadhir Al-Ansari's top articles?

The articles with the titles of

Long-term application of agronomic management strategies effects on soil organic carbon, energy budgeting, and carbon footprint under rice–wheat cropping system

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique

Hybrid river stage forecasting based on machine learning with empirical mode decomposition

An Analytical Surveillance of Land Utilization and Expansion of Urban Areas Employing Remote Sensing Algorithms

Evaluation of the impacts of seawater integration to electrocoagulation for the removal of pollutants from textile wastewater

Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

Publisher Correction: Long‑term application of agronomic management strategies effects on soil organic carbon, energy budgeting, and carbon footprint under rice–wheat cropping …

A simplistic approach for monitoring meteorological drought over arid regions: a case study of Rajasthan, India

...

are the top articles of Nadhir Al-Ansari at Luleå tekniska Universitet.

What are Nadhir Al-Ansari's research interests?

The research interests of Nadhir Al-Ansari are: Water resources, Environment, Geology, Civil Engineering

What is Nadhir Al-Ansari's total number of citations?

Nadhir Al-Ansari has 17,508 citations in total.

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