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Integrating machine learning and remote sensing-environmental-soil variables for estimating soluble and exchangeable potassium in dryland regions

Domain:

agriculture

Record type:

paper
Creator:
MagMusMohMoh
Host:avatar

In this study, the random forest (RF) algorithm and statistical model based on environmental covariates (ECOVs) representing climate, biota, topography, soil types, texture class, and selected properties including saturation percentage (SP), particle size fractions (clay, silt, sand), pH, soil organic matter (SOM), carbonate content (CaCO3), and cation exchangeable capacity (CEC) were utilized to develop pedotransfer functions (PTFs) examining the relationships between water-soluble potassium (WSK) and exchangeable potassium (ExchK) as responses to these variables in various soils in drylands of Sudan.

Visit

figshare.com

Tags

Agricultural land managementAgricultural land planningAgricultural management of nutrientsComputational modelling and simulation in earth sciencesBioclimatic covariatesplant nutrientsPedotransfer FunctionsRandom forestSoil-plant-interaction

Licenses

CC BY 4.0

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