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:

• Google Scholar

🎓 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)