Record Details

Geospatial Modelling for Delineation of Crop Management Zones Using Local Terrain Attributes and Soil Properties

KRISHI: Publication and Data Inventory Repository

View Archive Info
 
 
Field Value
 
Title Geospatial Modelling for Delineation of Crop Management Zones Using Local Terrain Attributes and Soil Properties
Not Available
 
Creator Roomesh Kumar Jena
Siladitya Bandyopadhyay
Upendra Kumar Pradhan
Pravash Chandra Moharana
Nirmal Kumar
Gulshan Kumar Sharma
Partha Deb Roy
Dibakar Ghosh
Prasenjit Ray
Shelton Padua
Sundaram Ramachandran
Bachaspati Das
Surendra Kumar Singh
Sanjay Kumar Ray
Amnah Mohammed Alsuhaibani
Ahmed Gaber
Akbar Hossain
 
Subject management zone
digital soil mapping
environmental covariates
possibilistic fuzzy c-means clustering
geographically weighted principal component analysis
 
Description Not Available
Defining nutrient management zones (MZs) is crucial for the implementation of sitespecific
management. The determination of MZs is based on several factors, including crop, soil,
climate, and terrain characteristics. This study aims to delineate MZs by means of geostatistical and
fuzzy clustering algorithms considering remotely sensed and laboratory data and, subsequently, to
compare the zone maps in the north-eastern Himalayan region of India. For this study, 896 grid-wise
representative soil samples (0–25 cm depth) were collected from the study area (1615 km2). The
soils were analysed for soil reaction (pH), soil organic carbon and available macro (N, P and K)
and micronutrients (Fe, Mn, Zn and Cu). The predicted soil maps were developed using regression
kriging, where 28 digital elevation model-derived terrain attributes and two vegetation derivatives
were used as environmental covariates. The coefficient of determination (R2) and root mean square
error were used to evaluate the model’s performance. The predicted soil parameters were accurate,
and regression kriging identified the highest variability for the majority of the soil variables. Further,
to define the management zones, the geographically weighted principal component analysis and
possibilistic fuzzy c-means clustering method were employed, based on which the optimum clusters
were identified by employing fuzzy performance index and normalized classification entropy. The
management zones were constructed considering the total pixel points of 30 m spatial resolution
(17, 86,985 data points). The area was divided into four distinct zones, which could be differently
managed. MZ 1 covers the maximum (43.3%), followed by MZ 2 (29.4%), MZ 3 (27.0%) and MZ 4
(0.3%). The MZs map thus would not only serve as a guide for judicious location-specific nutrient
management, but would also help the policymakers to bring sustainable changes in the north-eastern
Himalayan region of India.
Not Available
 
Date 2022-05-12T11:37:35Z
2022-05-12T11:37:35Z
2022-04-27
 
Type Article
 
Identifier Jena, R.K.; Bandyopadhyay, S.; Pradhan, U.K.; Moharana, P.C.; Kumar, N.; Sharma, G.K.; Roy, P.D.; Ghosh, D.; Ray, P.; Padua, S.; et al. Geospatial Modelling for Delineation of Crop Management Zones Using Local Terrain Attributes and Soil Properties. Remote Sens. 2022, 14, 2101. https://doi.org/10.3390/ rs14092101
2072-4292
http://krishi.icar.gov.in/jspui/handle/123456789/72092
 
Language English
 
Relation Not Available;
 
Publisher MDPI