Estimating the spatial distribution of soil available trace elements by combining auxiliary soil property data through the Bayesian maximum entropy technique

Fei, Xufeng; Lou, Zhidong; Xiao, Rui; Ren, Zhiyuan; Lv, Xiaonan

doi:10.1007/s00477-021-02104-y

Cited by 7 publications

(4 citation statements)

References 45 publications

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“…In accordance with the parameters of the adjusted semivariograms (Table 4), it is recognized that for the geostatistical method of ordinary cokriging, the exponential model is satisfactory to elucidate the behavior of trace elements versus terrain elevation. It corroborates with the minimum possible values of ASR, ASE, AIC and maximum R² value, providing an exact measure of how well the model fitted the semivariogram data (Fei et al, 2021).…”

Section: Ordinary Cokrigingsupporting

confidence: 74%

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Spatial variability of Cu, Zn and Mn contents in Non-anthropized soils in the Santa Catarina state

Cristofolini,

Miquelluti,

Campos

et al. 2023

Rev. DELOS

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The aim of this study was to evaluate the spatial variability of Cu, Zn and Mn in non-anthropic soils in the Santa Catarina state, using ordinary kriging and ordinary cokriging models. The Cu, Mn and Zn contents were taken from the database of the Department of Soils and Natural Resources of the State University of Santa Catarina. The geostatistical methods of ordinary kriging and ordinary cokriging were applied to fit the theoretical models to the experimental semivariograms. The model choice with the best fit was based on the parameters of Average Square Root, Average Standard Error, Akaike Criterion and Spatial Dependency Index. For ordinary kriging the Gaussian model (R² 0.998) explains the spatial variability of Cu, while the circular model describes the variations of Mn (R² 0.894) and Zn (R² 0.991). For ordinary cokriging, the exponential model describes the behavior of trace elements versus land elevation. Geostatistical methods are effective in the spatializing of Cu, Mn and Zn contents in non-anthropic soils in the Santa Catarina state. Finally, land elevation does not show a linear relationship with the trace elements levels, which are concentrated in the western region, correlating with the source material.

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Section: Ordinary Cokrigingsupporting

confidence: 74%

“…The SDI totals 100% for both independent variables, indicating weak spatial dependence even over large distances (Belkhiri et al, 2020;Fei et al, 2021). This behavior for Cu, Mn and Zn is explained by intrinsic variations in the soil, such as texture, mineralogy and material of origin (Costa et al, 2012;Ramzan and Wani, 2018).…”

Section: Ordinary Krigingmentioning

confidence: 98%

Spatial variability of Cu, Zn and Mn contents in Non-anthropized soils in the Santa Catarina state

Cristofolini,

Miquelluti,

Campos

et al. 2023

Rev. DELOS

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“…The study area is located in the lower reaches of the Yangtze River, where the main landform is a plain, and the topography is relatively consistent across the study area [38]. Moreover, this area is mainly used for rice planting (paddy field), and the soil type is dominated by paddy soil developed from sedimentary and alluvial parent materials [22,23,39]. Hence, topographic factors (EL, SL, and LULC) as well as the soil type and soil parent material variation revealed no significant influence on the variation in the cultivated land quality.…”

Section: Detection Of Factors Affecting Cultivated Land Quality Changesmentioning

confidence: 99%

“…The study area-the Hang-Jia-Hu Plain located in the lower reaches of the Yangtze River-is the most important food production base for Zhejiang Province and is regarded as one of the primary granaries during the history of agricultural production in China. Although the rapid development of urbanization and industrialization in recent decades has caused a decline in cultivated land, the government has taken corresponding reasonable measures, such as land consolidation and the promotion of soil testing and formula fertilization [22,23], to improve the quantity and quality of cultivated land. Considering that there are few studies on the variation in cultivated land quality and its main influential factors in this area, the main objectives of this research were (1) to explore the variation characteristics of cultivated land quality in the space-time domain and (2) to determine the main factors that influence the variation in cultivated land quality and to analyze the interactions between the factors.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Spatial–Temporal Variation in Cultivated Land Quality and Influential Factors in the Lower Reaches of the Yangtze River from 2017 to 2020

Wang

Fei

Sheng

et al. 2023

Land

Self Cite

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Cultivated land quality is directly related to national food security; hence, it is necessary to determine the spatial–temporal characteristics and factors that influence its variation. This study analyzed the soil properties and cultivated land quality in the Hang-Jia-Hu Plain, the most important grain production base in Zhejiang Province, located in the lower reaches of the Yangtze River, from 2017 to 2020. In addition, the factors that influenced cultivated land quality variation were explored. The results showed that soil pH and soil organic matter (SOM) significantly improved from 6.32 and 29.07 g/kg in 2017 to 6.38 and 31.54 g/kg in 2020, whereas the variations in available phosphorus (AP) and available potassium (AK) were not significant. More than 60% of the cultivated land still had the potential for soil nutrient status improvement. The cultivated land quality indicator (CLQI) calculated based on the national standard (GB/T 33469-2016) significantly increased from 0.90 in 2017 to 0.91 in 2020. According to the CLQI classification, approximately three quarters of the cultivated land was defined as high-yielding fields. Although the spatial pattern for CLQI was similar between 2017 and 2020, more than 75% of the cultivated land quality showed an increasing trend that was mainly located in the northeastern and central areas. The results of influential factor detection indicated that the improvement in SOM and available soil nutrients including AP and AK was the main reason for the CLQI increase, whereas the climate, topography, and socioeconomic factors had little influence on the change in CLQI. In addition, when influential factors interacted, a significant increase in the explanatory ability for CLQI was obtained, especially for the interaction of SOM and AP variation, which explained 41% of the CLQI variation. This study provides basic foundations and references for cultivated land quality monitoring and improvement in the lower reaches of the Yangtze River, China.

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Assessment and source apportionment of toxic metal in soils using integrated positive matrix factorization and Bayesian maximum entropy: A case study in Z county, southeastern China

Fei

Christakos

Lou³

et al. 2022

Ecological Indicators

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Estimating the spatial distribution of soil available trace elements by combining auxiliary soil property data through the Bayesian maximum entropy technique

Cited by 7 publications

References 45 publications

Spatial variability of Cu, Zn and Mn contents in Non-anthropized soils in the Santa Catarina state

Spatial variability of Cu, Zn and Mn contents in Non-anthropized soils in the Santa Catarina state

Exploring the Spatial–Temporal Variation in Cultivated Land Quality and Influential Factors in the Lower Reaches of the Yangtze River from 2017 to 2020

Assessment and source apportionment of toxic metal in soils using integrated positive matrix factorization and Bayesian maximum entropy: A case study in Z county, southeastern China

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