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

被引:9
|
作者
Jena, Roomesh Kumar [1 ]
Bandyopadhyay, Siladitya [2 ]
Pradhan, Upendra Kumar [3 ]
Moharana, Pravash Chandra [4 ]
Kumar, Nirmal [4 ]
Sharma, Gulshan Kumar [5 ]
Roy, Partha Deb [1 ]
Ghosh, Dibakar [1 ]
Ray, Prasenjit [6 ]
Padua, Shelton [7 ]
Ramachandran, Sundaram [8 ]
Das, Bachaspati [1 ]
Singh, Surendra Kumar [9 ]
Ray, Sanjay Kumar [2 ]
Alsuhaibani, Amnah Mohammed [10 ]
Gaber, Ahmed [11 ]
Hossain, Akbar [12 ]
机构
[1] Indian Inst Water Management, ICAR, Bhubaneswar 751023, India
[2] Natl Bur Soil Survey & Land Use Planning, Reg Ctr, ICAR, Kolkata 700091, India
[3] Indian Agr Res Inst, ICAR, New Delhi 110012, India
[4] Natl Bur Soil Survey & Land Use Planning, ICAR, Nagpur 440033, Maharashtra, India
[5] Indian Inst Soil & Water Conservat, Res Ctr, ICAR, Kota 324002, India
[6] Indian Agr Res Inst, ICAR, New Delhi 110012, India
[7] Cent Marine Fisheries Res Inst, ICAR, Kochi 682018, India
[8] Indian Inst Hort Res, ICAR, Bengaluru 560089, India
[9] Cent Coastal Agr Res Inst, ICAR, Old Goa 403402, India
[10] Princess Nourah Bint Abdulrahman Univ, Dept Phys Sport Sci, Coll Educ, POB 84428, Riyadh 11671, Saudi Arabia
[11] Taif Univ, Dept Biol, Coll Sci, POB 11099, At Taif 21944, Saudi Arabia
[12] Bangladesh Wheat & Maize Res Inst, Dept Agron, Dinajpur 5200, Bangladesh
关键词
management zone; digital soil mapping; environmental covariates; possibilistic fuzzy c-means clustering; geographically weighted principal component analysis; SPATIAL VARIABILITY; ORGANIC-CARBON; PREDICTION; NITROGEN; REGION; CLASSIFICATION; PHOSPHORUS; POLLUTION; WATER; IRON;
D O I
10.3390/rs14092101
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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(2)). 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-2) 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.
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页数:24
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