Dryland Soil Research (DLSR)

Dryland Soil Research (DLSR)


The primary publishing purpose for DLSR is to expand and promote scientific research, to develop frontiers of knowledge and to express the results of research in soil science and related subjects, and also for strengthening the collaborations among various specialists who are working in the fields of this endangered natural resource. Engaging with soil scientists globally and especially to our neighboring countries to present the regional soil conditions to the world, is our secondary goal. 

For the time being, no charge will be taken for publishing articles in Dryland Soil Research (DLSR).

The journal of Dryland Soil Research (DLSR) follows the open access procedures and there are no restrictions on access to all published and accepted articles.

This publication respects the regulations of ethics in publications and is subject to the rules of the Ethics Committee in Publication (COPE) and follows the executive regulations of the Law on Prevention and Combating Fraud in Scientific Works.

Authors retain the full publishing rights only under any license allowed by the journal.

 

ATTENTION: The title of the Journal of Soil Science Society of Iran (JSSSI) has been changed to Dryland Soil Research (DLSR).
The articles from Volume 1, Issue 1 of JSSSI have been republished in DLSR.

 

 

 

 

 

Keywords Cloud

  • Soil health
  • Nitrogen
  • Phycobili Protein
  • Siderophore
  • Remote sensing
  • Auxin
  • phosphorus
  • Phosphate
  • Bioremediation
  • modeling
  • Artificial Neural Network
  • Porosity
  • Sensitivity analysis
  • polyethylene
  • Irrigation
  • Spectral indices
  • WatSuit
  • Ilam Dam
  • extreme flood
  • lateral confinement index
  • Reach
  • stream power
  • FLOOCV approach
  • PLS-algorithm
  • soil conservation
  • textural constituents
  • Blue carbon
  • Carbon sequestration
  • Coastal areas
  • Ecosystem services
  • Sediment organic carbon
  • Bacteria
  • Biosurfactant production
  • Petroleum hydrocarbons
  • 16S rRNA
  • Microorganisms
  • Oil-contaminated
  • Petroleum-contaminated soil
  • Remediation
  • Soil pollution
  • machine learning
  • Vados zone
  • pollution
  • hydrocarbon
  • refinery
  • Compression curve
  • moldboard plowing
  • Chisel plowing
  • Lathyrus
  • Pre-compression stress
  • Swelling index
  • Potato varieties
  • Nutrient loss
  • soil erosion
  • tuber crops
  • Tuber morphology
  • vapor condensations
  • object-oriented
  • pixel-based
  • Satellite image
  • MPSIAC model
  • Ammonium Acetate
  • Arid and Semi-Arid Areas
  • Available Potassium
  • Extraction
  • Fertilizer Recommendation
  • Soil Test
  • Fertilizer management
  • Plum
  • Chloride transport fate
  • clay layer
  • HYDRUS-1D
  • Potassium chloride
  • Loess soil
  • water and wind erosion
  • mineral amendments
  • biological amendment
  • runoff
  • Mycorrhiza
  • Phosphate solubilizing bacteria
  • Rock Phosphate
  • Tea plant
  • Phosphate fertilizers
  • Biocrusts
  • b-fabric
  • pedo-feature
  • Pistachio
  • Satellite imagery
  • Soil Salinity
  • Proximal sensing
  • Environmental stress
  • Soil degradation
  • Sustainable agriculture
  • leaching
  • Magnesium Hazard
  • Salinity Hazard
  • Sodium Hazard
  • Soil remediation
  • Adsorbent
  • Cyanobacteria
  • Nano
  • Heavy Metals
  • Biochar
  • Compost
  • Microbial inoculants
  • Quality of plant
  • Geostatistic
  • Soil Quality
  • Water Quality
  • Water Resources
  • Water Salinity
  • Excel-based tool
  • potassium fraction
  • precision agriculture
  • soil fertility
  • Maize
  • Sulfur
  • Thiobacillus
  • Precision irrigation
  • Inverse modeling
  • Soil hydraulic properties
  • Vadose Zone
  • Human health
  • Plant
  • Plastic
  • Pedo-transfer function
  • soil function modeling
  • Water retention curve
  • multiple regression
  • Potassium fractions
  • Soil physicochemical properties
  • Genetic algorithm optimization
  • potassium
  • DeepLIFT feature attribution
  • nanoparticles
  • salinity
  • cadmium
  • microstructure
  • Khuzestan
  • spectroscopy
  • artificial neural networks

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