Md. Saiful Islam | Soil Science and Fertility Management | Best Researcher Award

Best Researcher Award

Md. Saiful Islam
Patuakhali Science and Technology University

Md. Saiful Islam
Affiliation Patuakhali Science and Technology University
Country Bangladesh
Scopus ID 60381573500
Documents 172
Citations 8,215
h-index 49
Subject Area Soil Science and Fertility Management
Event Agri Scientist Awards
ORCID 0000-0002-3598-0315

Md. Saiful Islam is a researcher affiliated with Patuakhali Science and Technology University in Bangladesh, with a stated subject area of Soil Science and Fertility Management. His scholarly profile records 172 documents, 8,215 citations, and an h-index of 49. These indicators provide a bibliometric context for considering his research activity and potential suitability for recognition through the Agri Scientist Awards. The profile information presented here is based on the supplied researcher identifiers and publication metrics, while the broader significance of soil science and fertility management is considered within contemporary agricultural research.

Abstract

Md. Saiful Islam is affiliated with Patuakhali Science and Technology University, Bangladesh, and is identified with Soil Science and Fertility Management. The supplied bibliometric profile records 172 documents, 8,215 citations, and an h-index of 49. These indicators describe a substantial body of indexed scholarly output and citation activity. His research profile can therefore be examined in relation to agricultural soil management, fertility, productivity, and sustainable resource use. This article summarizes the supplied academic identifiers, publication indicators, research orientation, potential impact, and suitability for consideration in the Agri Scientist Awards using neutral, evidence-based academic language and accessible profile information.

Keywords

Md. Saiful Islam, Soil Science, Fertility Management, Agricultural Research, Soil Management, Patuakhali Science and Technology University, Bangladesh, Bibliometrics, Sustainable Agriculture, Agri Scientist Awards

Introduction

Soil science and fertility management are central components of agricultural research because soil properties, nutrient availability, organic matter, and management practices influence crop productivity and the long-term sustainability of agricultural systems. Research in this field contributes to understanding how soils can be managed efficiently while maintaining productive capacity and environmental quality. Soil carbon and fertility management are also connected with wider agricultural and environmental objectives.[4] Within this context, the supplied profile of Md. Saiful Islam places his scholarly work within a subject area of direct relevance to agricultural science.

Research Profile

Md. Saiful Islam is associated with Patuakhali Science and Technology University in Bangladesh. The supplied Scopus author identifier is 60381573500, while the supplied ORCID identifier is 0000-0002-3598-0315. His stated subject area is Soil Science and Fertility Management. The supplied Scopus metrics comprise 172 documents, 8,215 citations, and an h-index of 49. These measures provide quantitative indicators of indexed publication activity and citation visibility, although bibliometric indicators should be interpreted alongside the quality, relevance, and contribution of individual research outputs.[1]

Research Contributions

The identified research area of Soil Science and Fertility Management encompasses the scientific assessment and management of soil resources for agricultural purposes. Such research may address nutrient availability, soil quality, fertility practices, productivity, organic matter, and sustainable soil-resource use. The supplied profile establishes the researcher’s disciplinary positioning, while detailed claims about individual research findings should be evaluated from the underlying publications rather than inferred solely from citation metrics.

Publications

The supplied profile reports 172 indexed documents associated with Md. Saiful Islam. This publication count indicates a substantial volume of scholarly output within the indexed record. A complete assessment of publication quality would require examination of individual articles, journals, citation contexts, authorship roles, research methods, and subject-specific contributions. The Scopus author identifier provides a mechanism for accessing the indexed publication record and distinguishing the researcher from other authors with similar names.[1]

Research Impact

The supplied citation count of 8,215 and h-index of 49 indicate notable citation activity within the stated scholarly profile. Citation measures can help describe the visibility and uptake of research but do not independently establish research quality or societal impact. For soil science, research relevance can additionally be assessed through methodological rigor, reproducibility, agricultural applicability, contribution to soil-resource management, and influence on subsequent scientific work. Broader soil-management research has demonstrated the importance of soil processes for agricultural and environmental sustainability.[4]

Award Suitability

Based on the supplied information, Md. Saiful Islam has an academic affiliation in agricultural science, a stated specialization in Soil Science and Fertility Management, 172 indexed documents, 8,215 citations, and an h-index of 49. These factors provide a reasonable bibliometric and disciplinary basis for consideration for a researcher recognition program such as the Agri Scientist Awards. Final award eligibility or selection should, however, be determined according to the official award criteria, independent verification of the profile, and assessment of the researcher’s substantive scholarly contributions.[5]

Conclusion

Md. Saiful Islam’s supplied academic profile identifies Patuakhali Science and Technology University as his institutional affiliation and Soil Science and Fertility Management as his subject area. The reported publication and citation indicators provide a useful quantitative overview of his indexed research activity. Together with the researcher’s persistent identifiers, these data support a structured academic recognition profile. Any formal assessment should supplement bibliometric information with direct evaluation of publications, research quality, originality, relevance, and documented contributions to agricultural science.

References

  1. Elsevier. (n.d.). Scopus author details: Md. Saiful Islam, Author ID 60381573500. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=60381573500
  2. ORCID. (n.d.). ORCID record: Md. Saiful Islam, ORCID 0000-0002-3598-0315.
    https://orcid.org/0000-0002-3598-0315
  3. Google Scholar. (n.d.). Md. Saiful Islam — Google Scholar profile.
    https://scholar.google.com/citations?user=PX7QF2gAAAAJ&hl=en
  4. Islam, M.S., Antu, U.B., Akter, R. et al. Preliminary Assessment of Essential and Potentially Toxic Elements in the Most Common Spices in a Developing Country: Health Hazard Implication. Biol Trace Elem Res 203, 6061–6078 (2025).
    https://doi.org/10.1007/s12011-025-04625-x
  5. Agri Scientist Awards. (2026). Official awards website.
    https://agriscientist.org/

Mohammad Reza Pahlavan Rad | Soil Science | Best Researcher Award

Dr. Mohammad Reza Pahlavan Rad | Soil Science | Best Researcher Award

Agricultural Research Education And Extension Organization (AREEO), Iran

Dr. Mohammad Reza Pahlavan-Rad is an Associate Professor at the Soil and Water Research Department of AREEO, Gorgan, Iran. He holds a Ph.D. in Soil Science from Gorgan University of Agricultural Sciences and Natural Resources, with a specialization in digital soil mapping, soil salinity, and machine learning applications in soil science.

Profile:

🎓 Education:

  • Ph.D. in Soil Science, Gorgan University of Agricultural Sciences & Natural Resources (2014)
    Thesis: Mapping and Updating Soil Map Using Random Forest and Multinomial Logistic Regression

  • M.S. in Soil Science, Gorgan University of Agricultural Sciences & Natural Resources (2006)
    Thesis: Effects of Irrigation Systems on Soil Moisture, Salinity, and Nutrient Uptake in Wheat

  • B.S. in Soil Science, Guilan University (1998)

👨‍🏫 Academic Positions:

  • Associate Professor, Dept. of Soil Science (2023–Present)

  • Assistant Professor, Dept. of Soil Science (2014–2023)

🔬 Research Focus:

Dr. Pahlavan-Rad’s work specializes in digital soil mapping, soil salinity modeling, soil texture and organic carbon prediction, and application of machine learning (e.g., Random Forest, MLR) in soil science. His regional expertise includes arid and floodplain landscapes in Iran and collaborative research in Europe, particularly the Czech Republic.

Citation Metrics:

  • Total Citations: 1,132

  • Citations Since 2020: 833

  • h-index: 13 (since 2020: 12)

  • i10-index: 15 (since 2020: 14)

Publication Top Notes:

  • Updating Soil Survey Maps Using Random Forest and Conditioned Latin Hypercube Sampling in the Loess Derived Soils of Northern Iran
    Geoderma, 232, 97–106. (2014)

  • Development and Analysis of the Soil Water Infiltration Global Database
    Earth System Science Data, 10(3), 1237–1263. (2018)

  • Response of Wheat Plants to Zinc, Iron, and Manganese Applications and Uptake and Concentration of Zinc, Iron, and Manganese in Wheat Grains
    Communications in Soil Science and Plant Analysis, 40(7–8), 1322–1332. (2009)

  • Spatial Variability of Soil Texture Fractions and pH in a Flood Plain (Case Study from Eastern Iran)
    Catena, 160, 275–281. (2018)

  • Application of Artificial Neural Networks to Predict the Final Fruit Weight and Random Forest to Select Important Variables in Native Population of Melon (Cucumis melo L.)
    Scientia Horticulturae, 181, 108–112. (2015)

  • Prediction of Soil Water Infiltration Using Multiple Linear Regression and Random Forest in a Dry Flood Plain, Eastern Iran
    Catena, 194, 104715. (2020)

  • Legacy Soil Maps as a Covariate in Digital Soil Mapping: A Case Study from Northern Iran
    Geoderma, 279, 141–148. (2016)

  • Predicting Regional Spatial Distribution of Soil Texture in Floodplains Using Remote Sensing Data: A Case of Southeastern Iran
    Catena, 182, 104149. (2019)

  • Predicting Soil Organic Carbon Concentrations in a Low Relief Landscape, Eastern Iran
    Geoderma Regional, 15, e00195. (2018)

  • Digital Soil Mapping of Deltaic Soils: A Case of Study from Hirmand (Helmand) River Delta
    Geoderma, 313, 233–240. (2018)

  • Effects of Potassium Rates and Irrigation Regimes on Yield of Forage Sorghum in Arid Regions
    International Journal of Agronomy and Agricultural Research, 6(4), 207–212. (2015)

  • Digital Soil Mapping Using Random Forest Model in Golestan Province
    Journal of Water and Soil Conservation, 21(6), 73–93. (2015)

  • Prediction of Soil Properties Using Random Forest with Sparse Data in a Semi-Active Volcanic Mountain
    Eurasian Soil Science, 53(9), 1222–1233. (2020)

  • Effects of Application of Zn, Fe and Mn on Yield, Yield Component, Nutrient Concentration and Uptake in Wheat Grain
    Pajouhesh & Sazandegi, 79, 142–150. (2008)

  • Nutrient Uptake, Soil and Plant Nutrient Contents, and Yield Components of Wheat Plants Under Different Planting Systems and Various Irrigation Frequencies
    Journal of Plant Nutrition, 34(8), 1133–1143. (2011)

  • Application of Generalized Additive Model and Classification and Regression Tree to Estimate Potential Habitat Distribution of Range Plant Species (Case Study: Khazri …)
    Iranian Journal of Range and Desert Research, 27(3), 561–576. (2020)

  • Application of Random Forest Method for Predicting Soil Classes in Low Relief Lands (Case Study: Hirmand County)
    Journal of Water and Soil Conservation, 24(1), 67–84. (2017)

  • Spatial Prediction of WRB Soil Classes in an Arid Floodplain Using Multinomial Logistic Regression and Random Forest Models, South-East of Iran
    Arabian Journal of Geosciences, 13, 1–11. (2020)

  • Digital Soil Mapping of Soil Classes in Floodplain and Low Relief Lands (Case Study: Hirmand County)
    Journal of Water and Soil Resources Conservation, 9(4), 107–120. (2020)

  • Digital Soil Mapping Using Machine Learning-Based Methods to Predict Soil Organic Carbon in Two Different Districts in the Czech Republic
    Soil & Water Research, 19(1). (2024)

  • Predicting Spatial Variability of Soil Salinity and Clay Content Using Geostatistics and Artificial Neural Networks Methods (Short Technical Report)
    Journal of Soil Management and Sustainable Production, 6(1), 247–254. (2016)

  • Responses of Wheat Plants in Terms of Soil Water Content, Bulk Density, Salinity, and Root Growth Under Different Planting Systems and Various Irrigation Frequencies
    Journal of Plant Nutrition, 33(6), 874–888. (2010)

  • Digital Modeling of Surface and Subsurface Soil Salinity in Golestan Province, Iran
    Geoderma Regional, 37, e00800. (2024)

  • Modeling Wheat Yield Using Some Soil Properties at the Field Scale (Case Study: Sistan Dam Research Farm, University of Zabol)
    Agricultural Engineering, 44(1), 81–95. (2021)

  • Preparation of Three-Dimensional Maps of Soil Particle Size Fractions by Combining Quantile Regression Forest Algorithm and Spline Depth Function in Golestan Province
    Iranian Journal of Soil and Water Research, 55(1), 51–68. (2024)