Variability of Silicon with Basic Soil Properties using Machine Learning

Author:

Pooniyan Seema Pooniyan Seema1,Tushar Tushar Kumar2,Kour Sarabdeep Kour Sarabdeep1,Yadav K.K. Yadav K.K.2

Affiliation:

1. Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu

2. Maharana Pratap University of Agriculture and Technology

Abstract

Abstract

Random geo-referenced soil surface (0–15 cm) samples were used to study the geographical variability of soil properties and plant-accessible silicon (Si) in rice growing soils of three districts of Jammu plains. The represent study was conducted descriptive statistics along with graphs for spatial distribution of soil data. The region had an interquartile range (IQR) of sand (%), silt (%), clay (%), pH, electrical conductivity (dSm− 1), soil organic carbon (g kg− 1), and cation exchange capacity (cmol (p+) kg− 1), 8.14, 5.80, 6.76, 1.10, 0.11, 2.70 and 1.88, respectively. The interquartile range (IQR) of available nitrogen (kg ha− 1), phosphorus (kg ha− 1), potassium (kg ha− 1), and sulfur (ppm) was noted to be 94.45, 16.93, 74.35, and 8.98, respectively. Positive skewness distribution was found in all physico-chemical properties, except sand content. Positive kurtosis distribution was found in all available nutrients except available nitrogen. Out of 10, 4 principal components exhibited greater than 1.00 eigen value and explained 89.00% cumulative variability among the parameters studied. The Dim 1 displayed 55.9% while Dim 2, Dim 3 and Dim 4 exhibited 16.9%, 10.3% and 5.9% variability, respectively amongthe parameters under study. This study will be helpful in identifying the variability contributing parameter and selection of suitable parameters in Jammu plains.

Publisher

Springer Science and Business Media LLC

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