Spatial variability of soil nutrients in forest areas: A case study from subtropical China

2018 ◽  
Vol 181 (6) ◽  
pp. 827-835 ◽  
Author(s):  
Wei Dai ◽  
Yuhuan Li ◽  
Weijun Fu ◽  
Peikun Jiang ◽  
Keli Zhao ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Soil Nutrients ◽  
Subtropical China

Spatial Variability Analysis of Soil Nutrients Based on GIS and Geostatistics: A Case Study of Yisa Township, Yunnan, China

2014 ◽  
Vol 5 (4) ◽  
pp. 348-355 ◽  
Author(s):  
Li Jing ◽  
Min Qingwen ◽  
Li Wenhua ◽  
Bai Yanying ◽  
Dhruba Bijaya G. C ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Soil Nutrients ◽  
Variability Analysis

scholarly journals Assessment of Spatial Variability of Soil Chemical Properties under Small-holder Farmers’ Field: a Case Study from Kavrepalanchowk District of Nepal

2018 ◽  
Vol 6 (3) ◽  
pp. 337-343
Author(s):  
Deepak Ghimire ◽  
Bandhu Raj Baral ◽  
Dinesh Panday
Keyword(s):  
Spatial Variability ◽  
Soil Nutrients ◽  
Soil Ph ◽  
Management Practices ◽  
Nutrient Management ◽  
Agricultural Land ◽  
Chemical Properties ◽  
Soil Samples ◽  
Soil Chemical Properties

A proper understanding of variability of soil chemical properties over an area is important for identifying the soil nutrients related production constraints. The suggestions for remedial measures and execution of appropriate nutrient management strategies are also based on the good knowledge of variation in soil nutrients. A case study was undertaken with an objective of assessing spatial variability of selected soil chemical properties of agricultural land. Soil samples from the surface (0 to 15 cm) were collected from farmers’ field in the central part of Kavrepalanchowk district of Nepal in February 2018. Soil samples were analyzed for soil pH, total organic carbon (TOC), total nitrogen (N), phosphorous (P), and potassium (K). Descriptive statistics revealed that the soil in the study area was slightly acidic and contained the low status of TOC, P, and K, while the N content was medium. Coefficient of variation (CV) indicated that soil pH was the least variable (CV= 9.37% with values ranging from 5.3 to 7.7) among the investigated soil samples while N was the most variable (CV= 98.81% with content values ranging from 0.03% to 0.67%). Other selected properties (TOC, P, and K) were found to be highly variable (CV= 49.94%, 94.89%, and 57.53% respectively). These variations in soil chemical properties were mostly related to the different cropping systems and soil management practices, including nutrient management carried out in the study area. Owing to the higher variability of nutrients, the results suggested to take into account various soil and nutrient management practices for sustained soil fertility and enhanced productivity.

Study on Spatial Variability of Soil Nutrients in Ebinur Lake Wetlands in China

2015 ◽  
Vol 73 ◽  
pp. 59-63 ◽  
Author(s):  
Yonghui Wang ◽  
Li Zhang ◽  
Yimiti Haimiti
Keyword(s):  
Spatial Variability ◽  
Soil Nutrients ◽  
Ebinur Lake ◽  
Lake Wetlands

scholarly journals Spatial variability of Soil Nutrients Using Geospatial Techniques: A case study in soils of Sanwer Tehsil of Indore district of Madhya Pradesh

2014 ◽  
Vol XL-8 ◽  
pp. 1353-1363 ◽  
Author(s):  
G. S. Tagore ◽  
G. D. Bairagi ◽  
R. Sharma ◽  
P. K. Verma
Keyword(s):  
Spatial Variability ◽  
Soil Nutrients ◽  
Spatial Dependence ◽  
Chemical Properties ◽  
Sampling Strategy ◽  
Spatial Prediction ◽  
Accurate Estimation ◽  
Suitable Alternative ◽  
Available N ◽  
Physico Chemical

A study was conducted to explore the spatial variability of major soil nutrients in a soybean grown region of Malwa plateau. From the study area, one hundred sixty two surface soil samples were collected by a random sampling strategy using GPS. Then soil physico-chemical properties i.e., pH, EC, organic carbon, soil available nutrients (N, P, K, S and Zn) were measured in laboratory. After data normalization, classical and geo-statistical analyses were used to describe soil properties and spatial correlation of soil characteristics. Spatial variability of soil physico-chemical properties was quantified through semi-variogram analysis and the respective surface maps were prepared through ordinary Kriging. Exponential model fits well with experimental semi-variogram of pH, EC, OC, available N, P, K, S and Zn. pH, EC, OC, N, P, and K has displayed moderate spatial dependence whereas S and Zn showed weak spatial dependence. Cross validation of kriged map shows that spatial prediction of soil nutrients using semi-variogram parameters is better than assuming mean of observed value for any un-sampled location. Therefore it is a suitable alternative method for accurate estimation of chemical properties of soil in un-sampled positions as compared to direct measurement which has time and costs concerned.

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