Mohammad Reza Pahlavan Rad | Soil Science | Best Researcher Award

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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)

 

Ayoub Lazaar | Soil Science | Best Researcher Award | 3887

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Dr. Ayoub Lazaar |  Soil Science | Best Researcher Award

Postdoctoral Researcher at Center for Sustainable soil Sciences, College for Sustainable Agriculture and Environmental Science, Mohammed VI Polytechnic University Of Benguerir, Morocco

Dr. Ayoub Lazaar is a Postdoctoral Researcher at the Center for Sustainable Soil Sciences (C3S), Mohammed VI Polytechnic University (UM6P), Morocco. He holds a Ph.D. in Geology and Geo-Environment and specializes in soil science, pedology, remote sensing, GIS, and spectroscopy (Vis-NIR/MIR). His research focuses on soil salinity assessment, spectral diagnostics of degradation, and geospatial modeling for sustainable land management. He has authored several peer-reviewed publications and developed Morocco’s first Vis-NIR soil spectral library. Dr. Lazaar has also led over 100 environmental impact assessments in his previous consultancy role. His innovations support precision agriculture and environmental sustainability in arid and semi-arid regions.

Profile:

🎓 Academic & Professional Background:

Dr. Ayoub Lazaar holds a Ph.D. in Geology and Geo-Environment and is currently a Postdoctoral Researcher at the Center for Sustainable Soil Sciences (C3S), UM6P. His expertise spans soil science, remote sensing, GIS, spectroscopy (Vis-NIR/MIR), geochemistry, and clay mineralogy. He has conducted over 100 Environmental Impact Assessments during his time as Lead Manager at ADA-Consulting.

🔬 Research Focus & Innovations:

  • 🧪 Soil salinity assessment using MIR spectroscopy and machine learning

  • 🛰️ Geospatial modeling for sustainable land management

  • 🔍 Spectral diagnostics of soil degradation in arid/semi-arid zones

  • 📚 Published 6+ peer-reviewed articles (2 under review)

  • 📊 Built the first Vis-NIR soil spectral library in the Triffa plain, Morocco

  • 🛠️ Developed low-cost sensors now adopted by FAO programs

🌍 Contributions & Impact:

Dr. Lazaar’s research bridges lab science with field applications, promoting cost-effective soil health diagnostics and sustainable agricultural practices. His latest focus includes developing pedotransfer functions for salinity prediction and analyzing groundwater quality in the Mediterranean region using advanced GIS and statistical tools.

🌐 Professional Affiliations:

  • IUSS (International Union of Soil Sciences)

  • SSSA (Soil Science Society of America)

  • EGU (European Geosciences Union)

  • CESFRA, UM6P

  • ASSS, MARS, and FAO Soil Spectroscopy Groups

Publication Top Notes:

  1. The application of proximal visible and near-infrared spectroscopy to estimate soil organic matter on the Triffa Plain of Morocco
    Lazaar, A., Mouazen, A.M., El Hammouti, K., Fullen, M., Pradhan, B., Memon, M.S., et al.
    International Soil and Water Conservation Research, 8(2), 195–204. (2020). [Citations: 35]

  2. Potential of VIS-NIR spectroscopy to characterize and discriminate topsoils of different soil types in the Triffa Plain (Morocco)
    Lazaar, A., Mahyou, H., Asagholizadeh, A., El Hammouti, K., et al.
    Soil Science Annual, 70(1), 54–63. (2019). [Citations: 14]

  3. The manifestation of VIS-NIRS spectroscopy data to predict and map soil texture in the Triffa Plain (Morocco)
    Lazaar, A., Pradhan, B., Zakariae, N., Abdelali, G., El Hammouti, K., Karim, A., et al.
    Kuwait Journal of Science, 48(1), 111–121. (2021). [Citations: 9]

  4. Physicochemical and mineralogical characterization of the Mediterranean soils of Triffa Plain (Northeast Morocco) by physicochemical analysis, X-ray diffraction and VIS spectroscopy
    Lazaar, A., El Hammouti, K., Andich, K., Hadria, R.
    ACM Conference Proceedings, (2020).

  5. Apport de la télédétection hyperspectrale dans la cartographie et la caractérisation des sols du Maroc Oriental
    Lazaar, A.
    (French publication), 2017.

 

Mohsen Hosseinalizadeh | Soil Erosion | Best Researcher Award

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Dr Mohsen Hosseinalizadeh | Soil Erosion | Best Researcher Award

Gorgan University of Agricultural Sciences and Natural Resources, Iran

Dr. Mohsen Hosseinalizadeh is an Associate Professor in Watershed Management Engineering at Gorgan University of Agricultural Sciences and Natural Resources (GUASNR), Iran. He holds a Ph.D. from the University of Tehran, where his research focused on optimizing sampling patterns for spatial simulation of erosion. His expertise includes soil erosion, spatial statistics, UAV applications in geo-hazards, and the evolution of loess landscapes. Dr. Hosseinalizadeh has authored numerous publications in high-impact journals and has supervised multiple MSc and Ph.D. students. He has received a scholarship for a sabbatical at the University of Münster, Germany, and actively contributes to environmental research and education.

Profile:

🎓 Education:

  • Ph.D. in Watershed Management Engineering, University of Tehran, Iran (2012)

  • M.Sc. in Watershed Management Engineering, Gorgan University of Agricultural Sciences and Natural Resources (2005)

  • B.Sc. in Rangeland and Watershed Management, Yazd University (2002)

🔬 Research Interests:

  • Soil erosion modeling & spatial statistics

  • UAV applications in geo-hazards

  • Loess landscape evolution

📚 Publications & Academic Contributions:

  • Authored 22+ research papers in leading journals such as Quaternary International, Geoderma, Catena, and Science of the Total Environment

  • Specializes in spatial geostatistical analysis, gully headcut susceptibility modeling, and digital soil mapping

  • Supervised 10 MSc and 4 Ph.D. students

🏆 Awards & Recognitions:

  • Sabbatical Scholarship (University of Münster, Germany) awarded by the Ministry of Science and Technology of Iran (2010-2011)

🎓 Teaching & Academic Roles:

  • Associate Professor at Gorgan University of Agricultural Sciences and Natural Resources (GUASNR), Iran

  • Courses taught: Geostatistics, Soil Erosion Modeling, Soil Bioengineering, and Climate Studies

Citations:

  • Total Citations: 706

  • Citations (Since 2020): 628

  • h-index: 13

  • h-index (Since 2020): 13

  • i10-index: 14

  • i10-index (Since 2020): 13

Publication Top Notes:

  • Gully headcut susceptibility modeling using functional trees, naïve Bayes tree, and random forest models – Geoderma, 2019.

  • Spatial modelling of gully headcuts using UAV data and four best-first decision classifier ensembles (BFTree, Bag-BFTree, RS-BFTree, and RF-BFTree) – Geomorphology, 2019.

  • How can statistical and artificial intelligence approaches predict piping erosion susceptibility? – Science of the Total Environment, 2019.

  • Evaluation of factors affecting gully headcut location using summary statistics and the maximum entropy model: Golestan Province, NE Iran – Science of the Total Environment, 2019.

  • GIS-based susceptibility assessment of the occurrence of gully headcuts and pipe collapses in a semi-arid environment: Golestan Province, NE Iran – Land Degradation & Development, 2019.

  • Spatial point pattern analysis of piping erosion in loess-derived soils in Golestan Province, Iran – Geoderma, 2018.

  • Change detection in piping, gully head forms, and mechanisms – Catena, 2021.

  • An application of different summary statistics for modelling piping collapses and gully headcuts to evaluate their geomorphological interactions in Golestan Province, Iran – Catena, 2018.

  • Gully head modelling in Iranian Loess Plateau under different scenarios – Catena, 2020.

  • Effect of soil sample size on saturated soil hydraulic conductivity – Communications in Soil Science and Plant Analysis, 2017.

  • Optimizing collapsed pipes mapping: Effects of DEM spatial resolution – Catena, 2020.

  • A review on the gully erosion and land degradation in Iran – Gully Erosion Studies from India and Surrounding Regions, 2020.

  • Combined effects of polyacrylamide and nanomagnetite amendment on soil and water quality, Khorasan Razavi, Iran – Journal of Environmental Management, 2018.

  • Comparison of soil erosion by ¹³⁷Cs and RUSLE-3D for loess deposits northeast of Iran (Study Area: Aghemam Catchment) – Journal of Water and Soil Conservation, 2014.

  • Analytical techniques for mapping multi-hazard with geo-environmental modeling approaches and UAV images – Scientific Reports, 2022.

  • Assessment of spatial variability of soil erodibility using geostatistics and GIS (Case study: Mehr watershed of Sabzevar) – Iranian Journal of Natural Resources, 2007.

  • Digital soil mapping and modeling in loess-derived soils of the Iranian Loess Plateau – Geocarto International, 2022.

  • Efficiency assessment of the EGEM to estimate gully erosion in Iky-Aghzly watershed of Golestan province – Journal of Water and Soil Conservation, 2017.

  • Multivariate geostatistical analysis of fallout radionuclides activity measured by in-situ gamma-ray spectrometry: Case study: Loessial paired sub-catchments in northeast Iran – Quaternary International, 2017.

  • Evaluation of various deep learning algorithms for landslide and sinkhole detection from UAV imagery in a semi-arid environment – Earth Systems and Environment, 2024.