Geospatial Modelling for Delineation of Crop Management Zones Using Local Terrain Attributes and Soil Properties
Defining nutrient management zones (MZs) is crucial for the implementation of site-specific 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 a...
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MDPI AG
2022-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/9/2101 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Roomesh Kumar Jena |
| collection | DOAJ |
| description | Defining nutrient management zones (MZs) is crucial for the implementation of site-specific 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 km<sup>2</sup>). 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 (R<sup>2</sup>) 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. |
| first_indexed | 2024-03-10T03:44:40Z |
| format | Article |
| id | doaj.art-2317396254ad476f8ea4999b290762ca |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:44:40Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-2317396254ad476f8ea4999b290762ca2023-11-23T09:10:34ZengMDPI AGRemote Sensing2072-42922022-04-01149210110.3390/rs14092101Geospatial Modelling for Delineation of Crop Management Zones Using Local Terrain Attributes and Soil PropertiesRoomesh Kumar Jena0Siladitya Bandyopadhyay1Upendra Kumar Pradhan2Pravash Chandra Moharana3Nirmal Kumar4Gulshan Kumar Sharma5Partha Deb Roy6Dibakar Ghosh7Prasenjit Ray8Shelton Padua9Sundaram Ramachandran10Bachaspati Das11Surendra Kumar Singh12Sanjay Kumar Ray13Amnah Mohammed Alsuhaibani14Ahmed Gaber15Akbar Hossain16ICAR—Indian Institute of Water Management, Bhubaneswar 751023, IndiaICAR—National Bureau of Soil Survey and Land Use Planning, Regional Centre, Kolkata 700091, IndiaICAR—Indian Agricultural Statistics Research Institute, New Delhi 110012, IndiaICAR—National Bureau of Soil Survey and Land Use Planning, Nagpur 440033, IndiaICAR—National Bureau of Soil Survey and Land Use Planning, Nagpur 440033, IndiaICAR—Indian Institute of Soil and Water Conservation, Research Centre, Kota 324002, IndiaICAR—Indian Institute of Water Management, Bhubaneswar 751023, IndiaICAR—Indian Institute of Water Management, Bhubaneswar 751023, IndiaICAR—Indian Agricultural Research Institute, New Delhi 110012, IndiaICAR—Central Marine Fisheries Research Institute, Kochi 682018, IndiaICAR—Indian Institute of Horticultural Research, Bengaluru 560089, IndiaICAR—Indian Institute of Water Management, Bhubaneswar 751023, IndiaICAR—Central Coastal Agricultural Research Institute, Old Goa 403402, IndiaICAR—National Bureau of Soil Survey and Land Use Planning, Regional Centre, Kolkata 700091, IndiaDepartment of Physical Sport Science, College of Education, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Biology, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Agronomy, Bangladesh Wheat and Maize Research Institute, Dinajpur 5200, BangladeshDefining nutrient management zones (MZs) is crucial for the implementation of site-specific 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 km<sup>2</sup>). 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 (R<sup>2</sup>) 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.https://www.mdpi.com/2072-4292/14/9/2101management zonedigital soil mappingenvironmental covariatespossibilistic fuzzy c-means clusteringgeographically weighted principal component analysis |
| spellingShingle | 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 Geospatial Modelling for Delineation of Crop Management Zones Using Local Terrain Attributes and Soil Properties Remote Sensing management zone digital soil mapping environmental covariates possibilistic fuzzy c-means clustering geographically weighted principal component analysis |
| title | Geospatial Modelling for Delineation of Crop Management Zones Using Local Terrain Attributes and Soil Properties |
| title_full | Geospatial Modelling for Delineation of Crop Management Zones Using Local Terrain Attributes and Soil Properties |
| title_fullStr | Geospatial Modelling for Delineation of Crop Management Zones Using Local Terrain Attributes and Soil Properties |
| title_full_unstemmed | Geospatial Modelling for Delineation of Crop Management Zones Using Local Terrain Attributes and Soil Properties |
| title_short | Geospatial Modelling for Delineation of Crop Management Zones Using Local Terrain Attributes and Soil Properties |
| title_sort | geospatial modelling for delineation of crop management zones using local terrain attributes and soil properties |
| topic | management zone digital soil mapping environmental covariates possibilistic fuzzy c-means clustering geographically weighted principal component analysis |
| url | https://www.mdpi.com/2072-4292/14/9/2101 |
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