scholarly journals Evaluation of soil fertility in the succession of karst rocky desertification using principal component analysis

2014 ◽  
Vol 6 (2) ◽  
pp. 3333-3359 ◽  
Author(s):  
L. W. Xie ◽  
J. Zhong ◽  
F. X. Cao ◽  
J. J. Li ◽  
L. C. Wu
Keyword(s):  
Principal Component Analysis ◽  
Microbial Biomass ◽  
Soil Fertility ◽  
Principal Component ◽  
Component Analysis ◽  
Hunan Province ◽  
Karst Rocky Desertification ◽  
Rocky Desertification ◽  
Microbial Biomass Nitrogen ◽  
Changing Trend

Abstract. Expanding of karst rocky desertification (RD) area in southwestern China has led to destructed ecosystem and local economic development lagging behind. It is important to understand the soil fertility at RD regions for the sustainable management of karst lands. The effects of the succession of RD on soil fertility were studied by investigating the stands and analyzing the soil samples with different RD grades in the central Hunan province, China, using the principal component analysis method. The results showed that the succession of RD had different impacts on soil fertility indicators. The changing trend of total organic carbon (TOC), total nitrogen (TN), available phosphorous (AP), microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN) out of 19 selected indicators in different RD regions was: potential RD (PRD) > light RD (LRD) > moderate RD (MRD) > intensive RD (IRD), whereas the changing trend of other indicators was not entirely consistent with the succession of RD. The degradation trend of soil fertility was basically parallel to the aggravation of RD, and the strength of integrated soil fertility was in the order of PRD > MRD > LRD > IRD. The TOC, total phosphorus (TP), cation exchange capacity (CEC), MBC, MBN, microbial mass phosphorous (MBP), and bulk density (BD) could be regarded as the key indicators to evaluate the soil fertility due to their close correlations to the integrated fertility.

scholarly journals Evaluation of soil fertility in the succession of karst rocky desertification using principal component analysis

Solid Earth ◽  
2015 ◽  
Vol 6 (2) ◽  
pp. 515-524 ◽  
Author(s):  
L. W. Xie ◽  
J. Zhong ◽  
F. F. Chen ◽  
F. X. Cao ◽  
J. J. Li ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Microbial Biomass ◽  
Soil Fertility ◽  
Principal Component ◽  
Component Analysis ◽  
Data Matrix ◽  
Karst Rocky Desertification ◽  
Rocky Desertification ◽  
Microbial Biomass Nitrogen ◽  
Changing Trend

Abstract. Expanding of karst rocky desertification (RD) area in southwestern China is strangling the sustainable development of local agricultural economy. It is important to evaluate the soil fertility at RD regions for the sustainable management of karst lands. The changes in 19 different soil fertility-related variables along a gradient of karst rocky desertification were investigated in five different counties belonging to the central Hunan province in China. We used principal component analysis method to calculate the soil data matrix and obtained a standardized integrate soil fertility (ISF) indicator to reflect RD grades. The results showed that the succession of RD had different impacts on soil fertility indicators. The changing trend of total organic carbon (TOC), total nitrogen (TN), available phosphorus, microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN) was potential RD (PRD) > light RD (LRD) > moderate RD (MRD) > intensive RD (IRD), whereas the changing trend of other indicators was not entirely consistent with the succession of RD. The degradation trend of ISF was basically parallel to the aggravation of RD, and the strength of ISF mean values were in the order of PRD > LRD > MRD > IRD. The TOC, MBC, and MBN could be regarded as the key indicators to evaluate the soil fertility.

scholarly journals Factor analysis for soil test data: a methodological approach in environment friendly soil fertility management

2007 ◽  
Vol 8 ◽  
pp. 8-19 ◽  
Author(s):  
Hari Dahal
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Soil Fertility ◽  
Test Data ◽  
Methodological Approach ◽  
Principal Component ◽  
Component Analysis ◽  
Soil Test ◽  
Soil Fertility Management ◽  
Fertility Management

Soil test data were used in factor analysis employing the Principal Component Analysis technique for the reduction and summarization of soil variables. Principal component analysis was found to be highly suggestive in analyzing soil test data on which a rational fertilizer nutrients recommendation can be made for a sustainable soil fertility management reign. The Journal of AGRICULTURE AND ENVIRONMENT Vol. 8, 2007, pp. 8-19

scholarly journals Principal Component Analysis on Soil Fertility Parameters of Vegetable Growing Panchayats/ Locations in Ernakulam District of Kerala

2020 ◽  
pp. 58-65
Author(s):  
A. Muhsina ◽  
Brigit Joseph ◽  
Vijayaraghava Kumar
Keyword(s):  
Principal Component Analysis ◽  
Soil Fertility ◽  
Soil Ph ◽  
Principal Component ◽  
Component Analysis ◽  
Organic Carbon Content ◽  
Score Plot ◽  
Five Factors ◽  
Total Variability ◽  
High Level

The present paper used Principal Component Analysis (PCA) on 13 soil fertility parameters including soil pH and electrical conductivity of 17 vegetable growing panchyat/locations in Ernakulam district of Kerala based on 583 soil samples. Soil pH of panchayats varied from 4.2- 5.8 with a coefficient of variation 3.16-12.23 per cent and it was inferred that most of the panchayats in the district had very strongly acidic (pH: 4.2-5) and strongly acidic soils (pH: 5-5.5). High level of organic carbon content was noticed in most of the panchayats except in four panchayats. The results of PCA revealed that five PC’s together explained a total variability of 80 per cent and the remaining PCs accounted for 20 per cent of the variability in the data which has been discarded from further analysis. First principal component accounted for 25 per cent variance followed by PC 2(21%), PC 3(14%), PC 4(10%) and PC 5(10%). Factor analysis generated five factors and they explained 85 per cent of variability. Score plot drawn as part of PCA showed that Chengamanadu, Manjapra and Thirumaradi panchayats had high content of soil available S and B. EC was also found to be higher in these panchayats. Amount of OC, Fe and Mn were more in Kalady, Keerampara and Mudakkuzha of Ernakulam district whereas Thuravur, Piravom and Pothanikkad had highly acidic and Mg rich soils. Amount of Zn was more in Vengoor panchayat. Available K, Ca, P and Cu were found to be higher in Kakkad, Nedumbassery, Vengola and Kadungalloor. Based on the fertility status of each panchayats, they could be classified into different groups.

scholarly journals Characterization of soil chemical properties of strawberry fields using principal component analysis

2013 ◽  
Vol 37 (1) ◽  
pp. 168-176 ◽  
Author(s):  
Gláucia Oliveira Islabão ◽  
Marília Alves Brito Pinto ◽  
Lisiane Priscila Roldão Selau ◽  
Ledemar Carlos Vahl ◽  
Luís Carlos Timm
Keyword(s):  
Principal Component Analysis ◽  
Organic Matter ◽  
Soil Fertility ◽  
Organic Matter Content ◽  
Chemical Properties ◽  
Principal Component ◽  
Component Analysis ◽  
Soil Chemical Properties ◽  
Available Phosphorus ◽  
Very High

One of the largest strawberry-producing municipalities of Rio Grande do Sul (RS) is Turuçu, in the South of the State. The strawberry production system adopted by farmers is similar to that used in other regions in Brazil and in the world. The main difference is related to the soil management, which can change the soil chemical properties during the strawberry cycle. This study had the objective of assessing the spatial and temporal distribution of soil fertility parameters using principal component analysis (PCA). Soil sampling was based on topography, dividing the field in three thirds: upper, middle and lower. From each of these thirds, five soil samples were randomly collected in the 0-0.20 m layer, to form a composite sample for each third. Four samples were taken during the strawberry cycle and the following properties were determined: soil organic matter (OM), soil total nitrogen (N), available phosphorus (P) and potassium (K), exchangeable calcium (Ca) and magnesium (Mg), soil pH (pH), cation exchange capacity (CEC) at pH 7.0, soil base (V%) and soil aluminum saturation(m%). No spatial variation was observed for any of the studied soil fertility parameters in the strawberry fields and temporal variation was only detected for available K. Phosphorus and K contents were always high or very high from the beginning of the strawberry cycle, while pH values ranged from very low to very high. Principal component analysis allowed the clustering of all strawberry fields based on variables related to soil acidity and organic matter content.

scholarly journals Expert Opinion Dimensions of Rural Landscape Quality in Xiangxi, Hunan, China: Principal Component Analysis and Factor Analysis

2020 ◽  
Vol 12 (4) ◽  
pp. 1316 ◽  
Author(s):  
Bin Wen ◽  
Jon Bryan Burley
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Public Space ◽  
Principal Component ◽  
Component Analysis ◽  
Alternative Interpretation ◽  
Rural Landscape ◽  
Hunan Province ◽  
Metric Properties ◽  
Rural Environments

Scholars and planning/design professionals are interested in the quantitative, metric properties influencing the quality and assessment of rural landscape space. These metrics are important for guiding rural planning, design, and construction of cultural rural environments. Respondents and metrics from four sampled villages (Qixin, Hangsha, Yanpai Xi, and Lvdong) in the Xiangxi District of Hunan Province in China were examined, employing statistical principal component analysis and factor analysis methods to understand the identifying properties concerning planning and design features of these rural mountain village landscape spaces. The two approaches reveal different aspects from the same variables. Through factor analysis and rotation, four general dimensions were revealed explaining approximately 62% of the variance: a settlement and environmental axis, an intangible culture axis, a productive landscape axis, and a transportation and public space axis, supporting the standing notion that the variables were ordinated across four dimensions in these mountain villages and occupied an elliptical plane that was different than the predicted space occupied by nearby cites. In contrast, principal component analysis revealed that the variables could be grouped into one latent dimension explaining 48% of the variance and revealing an alternative interpretation and spatial plot of the sites.

scholarly journals Principal Component Analysis on Soil Fertility Parameters in Vegetable Growing Locations of Kottayam District of Kerala

2021 ◽  
pp. 64-72
Author(s):  
A. Muhsina ◽  
Brigit Joseph ◽  
Vijayaraghava Kumar
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Organic Carbon ◽  
Soil Fertility ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Soil Testing ◽  
Score Plot

Present study utilizes Principal Component Analysis (PCA) of 13 soil testing variables obtained from 28 vegetable growing locations of Kottayam district and there were a total of 718 samples for analysis. Thirteen Principal Components (PCs) were generated and five PCs could explain the major share of variance (80%). Score plot was drawn based on PCA and the results indicated that none of the variables was predominant in Bharananganam, Kadanadu, Kozhuvanal, Kidangoor and Pallikkathode and also these panchayats had positive scores on both F1 and F2 when factor analysis was conducted. Boron (B), Copper (Cu) and Zinc (Zn) were predominant in Akalakkunnam, Kadalpalamattom, Meeaachil, Melukavu, Poonjar and Ramapuram panchayats. Elikulam, Erumeli, Karoor, Mundakkayam, Mutholi, Poonjar south, Thalapalm and Vakathanom were those panchayats where the contribution of Magnesium (Mg), Potassium (K) and pH was more. All other elements viz, Oxidisable Organic Carbon (OC), Sulphur (S), Phosphorus (P), Calcium (Ca), Manganese (Mn) and Iron (Fe) had significant importance in Ayarkkunnam, Aymanam, Chempu, Kaduthuruthy, Kurichi, Manjoor, Maravanthuruth, Puthuppally and Thalayazham panchayats.

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