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:

πŸŽ“ 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)

πŸ‘¨β€πŸ« Academic Positions:

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

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

πŸ”¬ 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)

 

Chitranjan Sharma | Soil Science and Fertility Management | Best Researcher Award

Chitranjan Sharma | Soil Science and Fertility Management | Best Researcher Award

Dr Chitranjan Sharma, Amity University Uttar Pradesh, Noida, India, India

Dr.Β  Dr Chitranjan Sharma is an accomplished academic with a strong background in agriculture and soil science. Holding a B.Sc. in Agriculture (1997) and an M.Sc. in Ag Chemistry & Soil Science (1999) from SSJMU Kanpur and the University of Allahabad, respectively, he earned a Gold Medal and distinction throughout his studies. He completed his D.Phil. in Science (2005), focusing on phytoremediation. Dr. [Name] has taught at various institutions, including the University of Allahabad and Amity University. Currently, he is an Assistant Professor and Program Leader at Amity University, specializing in soil science and crop production. πŸ§‘β€πŸ«πŸŒΎπŸ‘©β€πŸ”¬πŸ“˜

Publication Profile

Google Scholar

Education

With an extensive academic background, the individual began with a First Division (69.89%) in Natural and Social Sciences from Bihar School Examination Board in 1990. They continued excelling, securing First Division in I.Sc. Agriculture (66.60%) from UP Board in 1993. In 1997, they completed a B.Sc. in Agriculture from SSJMU Kanpur with 72.37%, focusing on Math, Statistics, Soil Conservation, and more. Earning an M.Sc. in Agricultural Chemistry and Soil Science from the University of Allahabad in 1999, they were a Gold Medalist (75.09%). In 2005, they completed a D.Phil. in Science, focusing on heavy metals and phytoremediation. πŸŒΏπŸ“˜πŸ₯‡

Teaching Experience

Currently serving as a faculty member at Amity University Uttar Pradesh, Noida since February 2020, the individual previously taught at the University of Allahabad, Prayagraj (2003-2007). There, they educated MSc (Ag) students in subjects like Agricultural Chemistry, Plant Biochemistry, Soil Fertility, and Soil Microbiology πŸŒΎπŸ“š. Over time, they have continued interacting with MSc and research scholars at the Sheila Dhar Institute of Soil Science. Currently, they teach courses including Fundamentals of Soil Science, Crop Ecology, Advances in Soil Fertility, and Agronomy of Kharif Crops to graduate and postgraduate students πŸŒ±πŸŽ“. Their expertise shapes future agronomists and soil scientists.

Research focus

Chitranjan Kumar’s research primarily focuses on soil science, environmental restoration, and bioremediation. His work includes studying heavy metals in soil, their management, and environmental restoration through bioremediation techniques. He also explores microbial synthesis of nanoparticles, particularly gold, and the use of organic inputs and bacterial inoculations to improve soil health and crop production. His studies contribute to sustainable agriculture, focusing on soil detoxification and enhancing soil quality. This interdisciplinary research is vital for environmental conservation and agricultural productivity. 🌱🧬πŸ§ͺπŸŒπŸ…

Publication Top Notes

Biotechnological advances in bioremediation of heavy metals contaminated ecosystems: an overview with special reference to phytoremediation

Production of Polyhydroxyalkanoates (PHAs) by Bacillus Strain Isolated from Waste Water and Its Biochemical CharacterizationIntegrated micro-biochemical approach for phytoremediation of cadmium and zinc contaminated soils

Integrated micro-biochemical approach for phytoremediation of cadmium and lead contaminated soils using Gladiolus grandiflorus L cut flower

Hyperaccumulator oilcake manure as an alternative for chelate-induced phytoremediation of heavy metals contaminated alluvial soils

Phytoaccumulation, Interaction, Toxicity and Remediation of Cadmium fromΒ Helianthus annuusΒ L. (sunflower)

Conclusion

Dr. Sharma’s innovative research, extensive publications, and leadership in academia make him a strong contender for the Best Researcher Award.