scholarly journals Peer Review #2 of "Spatial variability in soil pH and land use as the main influential factor in the red beds of the Nanxiong Basin, China (v0.2)"

2019 ◽  
Author(s):  
J Wang
Keyword(s):  
Land Use ◽  
Spatial Variability ◽  
Peer Review ◽  
Soil Ph ◽  
Influential Factor ◽  
Red Beds

scholarly journals Spatial variability of soil pH and land use as the main influential factor in redbeds of the Nanxiong Basin, China

2018 ◽  
Author(s):  
Ping Yan ◽  
Hua Peng ◽  
Luobin Yan ◽  
Shaoyun Zhang ◽  
Aimin Chen
Keyword(s):  
Land Use ◽  
Spatial Variability ◽  
Soil Ph ◽  
Nutrient Management ◽  
Soil Nutrient ◽  
Spherical Model ◽  
Influential Factor ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Main Factors

Soil pH is the main factor affecting soil nutrient availability and chemical substances in soil. It is of great significance to study the spatial variability of soil pH for soil nutrient management and soil pollution prediction. In order to explore the causes of spatial variability of soil pH in redbed areas, the Nanxiong Basin in south China was selected as an example, and soil pH was measured in the topsoil by nested sampling (0–20 cm depth). The spatial variability characteristics of the soil pH were analysed by geostatistics and classical statistical methods, and the main factors influencing the spatial variability of soil pH are discussed. The results showed that the coefficient of variation in the redbed areas of Nanxiong Basin was 17.18%, indicating moderate variability. The geostatistics analysis showed that the spherical model is the optimal theoretical model for explaining the soil pH’s variability, which is influenced by both structural and random factors. The spatial distribution and pattern analysis showed that soil pH content in the northeast and southwest is relatively high, and is lower in the northwest. These results indicate that topographic factors and land use patterns are the main factors.

scholarly journals Spatial variability in soil pH and land use as the main influential factor in the red beds of the Nanxiong Basin, China

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6342 ◽  
Author(s):  
Ping Yan ◽  
Hua Peng ◽  
Luobin Yan ◽  
Shaoyun Zhang ◽  
Aimin Chen ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Variability ◽  
Soil Ph ◽  
Soil Nutrient ◽  
Spherical Model ◽  
Influential Factor ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Factors Analysis ◽  
Red Bed

Soil pH is the main factor affecting soil nutrient availability and chemical substances in soil. It is of great significance to study the spatial variability of soil pH for the management of soil nutrients and the prediction of soil pollution. In order to explore the causes of spatial variability in soil pH in red-bed areas, the Nanxiong Basin in south China was selected as an example, and soil pH was measured in the topsoil by nested sampling (0–20 cm depth). The spatial variability characteristics of soil pH were analyzed by geostatistics and classical statistical methods, and the main factors influencing spatial variability in soil pH are discussed. The coefficient of variation in the red-bed areas of Nanxiong Basin was 17.18%, indicating moderate variability. Geostatistical analysis showed that the spherical model is the optimal theoretical model for explaining variability in soil pH, which is influenced by both structural and random factors. Analysis of the spatial distribution and pattern showed that soil pH is relatively high in the northeast and southwest, and is lower in the northwest. These results indicate that land use patterns and topographic factors are the main and secondary influencing factors, respectively.

scholarly journals Spatial variability of soil pH and land use as the main influential factor in redbeds of the Nanxiong Basin, China

2018 ◽  
Author(s):  
Ping Yan ◽  
Hua Peng ◽  
Luobin Yan ◽  
Shaoyun Zhang ◽  
Aimin Chen
Keyword(s):  
Land Use ◽  
Spatial Variability ◽  
Soil Ph ◽  
Nutrient Management ◽  
Soil Nutrient ◽  
Spherical Model ◽  
Influential Factor ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Main Factors

Soil pH is the main factor affecting soil nutrient availability and chemical substances in soil. It is of great significance to study the spatial variability of soil pH for soil nutrient management and soil pollution prediction. In order to explore the causes of spatial variability of soil pH in redbed areas, the Nanxiong Basin in south China was selected as an example, and soil pH was measured in the topsoil by nested sampling (0–20 cm depth). The spatial variability characteristics of the soil pH were analysed by geostatistics and classical statistical methods, and the main factors influencing the spatial variability of soil pH are discussed. The results showed that the coefficient of variation in the redbed areas of Nanxiong Basin was 17.18%, indicating moderate variability. The geostatistics analysis showed that the spherical model is the optimal theoretical model for explaining the soil pH’s variability, which is influenced by both structural and random factors. The spatial distribution and pattern analysis showed that soil pH content in the northeast and southwest is relatively high, and is lower in the northwest. These results indicate that topographic factors and land use patterns are the main factors.

scholarly journals Geostatistical Analysis on Spatial Variability of Soil Nutrients in Vertisols of Deccan Plateau Region of North Karnataka, India

2020 ◽  
Vol 5 (2) ◽  
pp. 283-295
Author(s):  
Vinod Tamburi ◽  
Amba Shetty ◽  
S. Shrihari
Keyword(s):  
Land Use ◽  
Spatial Variability ◽  
Soil Nutrients ◽  
Soil Ph ◽  
Agricultural Land ◽  
Microwave Plasma ◽  
Spherical Model ◽  
Atomic Emission ◽  
Deccan Plateau ◽  
Land Use And Management

Different methods of land use and management have a significant effect on soil properties distribution. Understanding of variations in soil nutrients in agricultural land use is important. An increase in extraction of nutrients, soil degradation, and management of nutrients is leading to a decline in quality of vertisols across the Deccan plateau of India. Though there are studies on spatial variability of vertisols macronutrients, studies on available calcium (Ca) and available magnesium (Mg) are rare. This study is conducted in Gulbarga taluk, north Karnataka, India, to evaluate the variability of soil pH, Ca, Mg, and Zinc (Zn). A total of 78 samples of soils are collected at 0 to 15 cm depth based on the accessibility and distribution of field patterns. Four subsamples represent a single composite sample. Agilent 4200 MP-AES (Microwave Plasma-Atomic. Emission Spectrometer) was used for determining the concentration of soil nutrients. The soil nutrients represent wide variation in coefficient of variation (CV) with a value of 6 % (for pH) to 70.9 % (for Zn). The soil pH showed a significantly positive correlation to Ca and a negative correlation to Mg. Geostatistical investigation indicates spherical model is the best fit for all nutrients. Except for Ca, all nutrients showed moderate spatial dependence. Ordinary kriging is used to generate spatial variability maps. The maps of spatial variability are highly variable in nutrients content and indicate that site-specific management needs to be taken by local authorities and improve the livelihood of marginal farmers and also for sustainable agriculture.

Land use drives the spatial variability of soil phosphorus in the Hexi Corridor, China

2021 ◽  
Author(s):  
Xiangdong Li ◽  
Tong Liu ◽  
Chunlei Zhao ◽  
Ming’an Shao ◽  
Jiong Cheng
Keyword(s):  
Land Use ◽  
Spatial Variability ◽  
Soil Phosphorus ◽  
Hexi Corridor

scholarly journals Spatial variability and mapping of soil fertility status in a high-potential smallholder farming area under sub-humid conditions in Zimbabwe

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Gabriel Soropa ◽  
Olton M. Mbisva ◽  
Justice Nyamangara ◽  
Ermson Z. Nyakatawa ◽  
Newton Nyapwere ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Soil Ph ◽  
Strong Evidence ◽  
Spatial Dependence ◽  
Random Component ◽  
Smallholder Farming ◽  
Mineral N ◽  
Spatial Trend ◽  
Spatial Trends

AbstractA study was conducted to examine spatial variability of soil properties related to fertility in maize fields across varying soil types in ward 10 of Hurungwe district, Zimbabwe; a smallholder farming area with sub-humid conditions and high yield potential. Purposively collected and geo-referenced soil samples were analyzed for texture, pH, soil organic carbon (OC), mineral N, bicarbonate P, and exchangeable K. Linear mixed model was used to analyze spatial variation of the data. The model allowed prediction of soil properties at unsampled sites by the empirical best linear unbiased predictor (EBLUP). Evidence for spatial dependence in the random component of the model was evaluated by calculating Akaike’s information criterion. Soil pH ranged from 4.0 to 6.9 and showed a strong spatial trend increasing from north to south, strong evidence for a difference between the home and outfields with homefields significantly higher and between soil textural classes with the sand clay loam fraction generally higher. Soil OC ranged from 0.2 to 2.02% and showed no spatial trend, but there was strong evidence for a difference between home and outfields, with mean soil OC in homefields significantly larger, and between soil textural classes, with soil OC largest in the sandy clay loams. Both soil pH and OC showed evidence for spatial dependence in the random effect, providing a basis for spatial prediction by the EBLUP, which was presented as a map. There were significant spatial trends in mineral N, available P and exchangeable K, all increasing from north to south; significant differences between homefields and outfields (larger concentrations in homefields), and differences between the soil textural classes with larger concentrations in the sandy clay loams. However, there was no evidence for spatial dependence in the random component, so no attempt was made to map these variables. These results show how management (home fields vs outfields), basic soil properties (texture) and other factors emerging as spatial trends influence key soil properties that determine soil fertility in these conditions. This implies that the best management practices may vary spatially, and that site-specific management is a desirable goal in conditions such as those which apply in Ward 10 of Hurungwe district in Zimbabwe.

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