Moderate Spatial Structure Research Articles

Micro-Scale Analysis and Optimization of Rural Settlement Spatial Patterns: A Case Study of Huanglong Town, Dayu County

Optimizing the spatial patterns of rural settlements is crucial for rural development and revitalization. Enhancing the internal spatial configuration of these settlements necessitates a comprehensive understanding of their micro-scale spatial characteristics. This study develops evaluation indicators and methodologies to quantify rural settlement spatial patterns by analyzing their multidimensional aspects. The research utilizes Huanglong Town in Dayu County, Jiangxi Province, as a case study for exploring micro-scale spatial patterns and proposing corresponding optimization models. The research employs remote sensing image processing and GIS spatial analysis to collect data on the study area. The results indicate that rural settlements in Huanglong Town generally form clustered patterns with moderate spatial structure intensity and order. Notably, spatial heterogeneity is observed across the northern mountainous area, the central plain and low hilly region, and the southern hilly area. Based on these findings, the study categorizes rural settlements in Huanglong Town into four optimization models: stable improvement, internal potential exploitation, controlled expansion, and relocation and withdrawal. Each model is associated with differentiated optimization strategies. By integrating analyses of spatial form, structure, and order, this study reveals the intrinsic spatial characteristics of rural settlements, offering a systematic approach to guide their spatial optimization.

Assessment of Complex Terminal Groundwater Aquifer for Different Use of Oued Souf Valley (Algeria) Using Multivariate Statistical Methods, Geostatistical Modeling, and Water Quality Index

This research aims to assess the hydrogeochemical evolution of the groundwater in Oued souf valley for drinking and irrigation purposes. To achieve this, 49 groundwater samples from the complex terminal were examined and treated concurrently with multivariate statistical methods, geostatistical modeling and the WQI (water quality index). Focusing on the physico-chemical parameters, Q mode clustering analysis detected four major water groups, where the mineralization augmented from group 1 to group 4. The hydro-chemical type was the same, Ca-Mg-Cl-SO4 for all the groups. Calcite, dolomite, anhydrite, and gypsum would be the dominant reactions with the undersaturation of evaporates minerals, based on geochemical modeling, while the carbonate minerals are precipitating. Geostatistical analysis using ordinary Kriging demonstrated the exponential semi-variogram model fitted for EC (electrical conductivity), Ca2+ (calcium), Mg2+ (magnesium), K+ (potassium), HCO3− (bicarbonate), Cl− (chloride), and SO42− (sulfate). At the same time, the rational quadratic model was the best-fitted semi-variogram model for Na+ (sodium) and NO3− (nitrate). EC, SO42−, and NO3− have a strong spatial structure, while Ca2+, Na+, K+, and HCO3− have a moderate spatial structure. Moreover, there was a weak spatial structure for Mg2+ and Cl−. The WQI shows that CT (complex terminal groundwater aquifers) are not suitable for drinking and their quality for irrigation fluctuates from excellent to moderate quality.

Groundwater quality assessment using multivariate analysis, geostatistical modeling, and water quality index (WQI): a case of study in the Boumerzoug-El Khroub valley of Northeast Algeria

In this study, the analytical data set of 26 groundwater samples from the alluvial aquifer of Boumerzoug-El khroub valley has been processed simultaneously with Multivariate analysis, geostatistical modeling, WQI, and geochemical modeling. Cluster analysis identified three main water types based on the major ion contents, where mineralization increased from group 1 to group 3. These groups were confirmed by FA/PCA, which demonstrated that groundwater quality is influenced by geochemical processes (water–rock interaction) and human practice (irrigation). The exponential semivariogram model fitted best for all hydrochemical parameters values and WQI. Groundwater chemistry has a strong spatial structure for Mg, Na, Cl, and NO3, and a moderate spatial structure for EC, Ca, K, HCO3, and SO4. Water quality maps generated using ordinary Kriging are consistent with the HCA and PCA results. All water groups are supersaturated with respect to carbonate minerals, and dissolution of kaolinite and Ca-smectite is one of the processes responsible for hydrochemical evolution in the area.

Relationship between Spatial Variability Pattern of Wheat Yield and Soil Properties

Aims: Determining effects of spatial variation of some soil properties on wheat quantity and quality variation in order that proper soil and inputs management can be applied for sustainable wheat production.
 Study Design: Analyzing data of a field with center pivot irrigation system and uniform management using the geostatistical method.
 Place and Duration of Study: Soil and Water Research Department, Fars Agricultural and Natural Resources Research and Education Center, Darab, Iran, from September 2013 to February 2014.
 Methodology: Wheat yield data harvested by class lexion 510 combine from 25 m2 plots (11340 locations) with the corresponding geographical location were used. Besides, soil properties and wheat yield were measured at 36 randomly selected points on the field. Interpolation of parameters was predicted with the best semi-variogram model using kriging, inverse distance weighted (IDW), and cokriging methods.
 Results: Results showed that wheat yield varied from 2 to 10.08 tons per hectare. Cokriging with cofactor of kernel weight interpolator had more accuracy compared to the combine default interpolator (kriging). A logical, linear correlation was found between different parameters. The best variogram model for pH, OC, and ρb was exponential, for EC, TNV, SP, soil silt and clay percentage was spherical, and for soil, percentage sand was Gaussian model. Data of soil sand, silt, and clay percentage, EC, TNV, and SP had strong spatial structure, and soil pH, OC, and ρb had moderate spatial structure. The best interpolation method for soil pH, EC, sand and silt percentage was kriging method; while, for TNV, SP, OC, ρb, and clay percentage was IDW.
 Conclusion: There was a close relationship between wheat yield variation and changes in the soil properties. Soil properties and wheat yield distribution maps provided valuable information which could be used for wheat yield improvement in precision agriculture.

Assessment of geostatistical and interpolation methods for mapping forest dieback intensity in Zagros forests

During recent years, oak decline has been widely spread across Brant’s oak (Quercus Brantii Lindl.) stands in the Zagros Mountains, Western Iran, which caused large-area forest dieback in several sites. Mapping the intensity and spatial distribution of forest dieback is essential for developing management and control strategies. This study evaluated a range of geostatistical and interpolation methods to explore the spatial structure and provide area-based maps of the intensity of forest dieback across a representative test site - Ilam Province - that was severely affected by Oak decline. The geostatistical analysis provided in-depth measures of the spatial structure amongst the selective sampling units (120 quadratic sample plots of 1200 m2), which eventually resulted in an area-based maps of dieback intensity. The accuracy of the applied methods was assessed by mean error percentage (%ME), root mean squared error percentage (%RMSE) and coefficient of determination (R2). Results showed moderate spatial structure within the sampling units. Moreover, cokriging (associated with soil humidity and aspect as independent variables) approach resulted in the highest accuracy, followed by two other methods of kriging and Radial Basis Function. Results suggested that cokriging can accurately estimate the intensity of dieback and its spatial distribution in the study area. According to this, an average dieback intensity of 18.12 % was estimated within the study area.

Spatio-Temporal Variability of Soil Beta-glucosidase Activity at the Arable Field Scale

Knowledge about the spatio-temporal variability of soil enzymatic properties is crucial in evaluating their structure-function relationship and their impact on ecosystem functions. The aim of this study was to assess the spatio-temporal variability of soil β-glucosidase (BG) activity against selected physico-chemical properties at the arable fild scale. A grid soil sampling (10×10 m) was used to measure the spatial variation of soil properties across a 0.4-ha fild. The BG activity was analyzed in soil samples that were collected from the upper 20 cm of Luvisols at 50 locations in April and August 2007 and 2008. Additionally, total organic carbon (CORG), total nitrogen (NTOT) concentrations, soil pHKCl as well as texture and water content were determined. The dataset was analyzed using classical statistical and geostatistical methods. Based on the analysis of variance, it was found that the BG activity, CORG, and water content showed signifiant differences between the four sampling dates. The BG activity showed a high contribution of nugget effect in sill (over 50%) and revealed a moderate spatial structure. The range of spatial autocorrelation calculated for the BG activity was between 15.0 and 61.0 m. Less intensive sampling grid should be recommended for soil BG activity in further studies concerning spatial variability in arable fild scale. In turn, more frequent sampling must be included in the sampling strategy in order to better understand whether the BG activity always shows permanent spatial patterns in soil or whether it is more randomized.

Spatio-temporal variability of soil sulfur content and arylsulfatase activity at a conventionally managed arable field

Sulfur (S) has long been recognized as essential for plant metabolism, but over the last 30years the increasing deficit of sulfur has become a problem all over the world. For this reason, more research should be devoted to determining the content and spatio-temporal variability of sulfur in relation to an improved management of this element. The aims of this study were to assess the spatio-temporal variability of soil sulfur forms and arylsulfatase activity against selected physicochemical properties on a plot scale.A grid soil sampling (10×10m) was used to measure the spatial variation of soil properties across a 0.4ha field. Soil samples were collected from the upper 27cm of Luvisols at 50 location in April and August 2007 and 2008. The total (STOT), organic (SORG) and available (SO42−-S) sulfur concentrations and arylsulfatase (AS) activity were analyzed. Additionally, total organic carbon (CORG), total nitrogen (NTOT) concentrations, soil pHKCl as well as texture and water content were determined. The dataset was analyzed using classical statistical and geostatistical methods.Based on the analysis of variance, it was found that soil properties, except for CORG, NTOT and pHKCl, showed significant differences between the four sampling dates (Tukey's test, p<0.05). Additionally, it was found that the temporal stability of the data set showed no clear time-associated shift based on Spearman's rank correlations coefficients. Relatively high degree of variability of the soil properties over time was also indicated by the mean relative differences (MRD). Generally, higher level of temporal stability was noted for the S forms and CORG and NTOT concentrations as compared to the AS activity. Among the studied properties, only AS activity at all sampling times and all of the S forms measured in August (except STOT in 2008 and SO42-S in 2007) revealed a spatial autocorrelation (Moran's I). All the studied properties revealed a high contribution of nugget effect in sill (over 50%) or pure nugget effect. Some properties revealed a moderate spatial structure (a nugget/sill ratio between 59.2 and 70.3%), whereas the concentration of SORG measured in August 2008 and AS activity analyzed in 2008 were in the weak variability class. Only AS activity in 2007 showed a pure nugget effect. The range of spatial autocorrelation calculated for the analyzed properties was between 20.0 and 34.0m.Results of this study are essential to understand and predict the contribution of internal factors in the total variability of soil properties that can interfere with the influence of soil management practices. The spatio-temporal variability showed that the studied area was temporally unstable and more frequent sampling must be included in the sampling strategy in order to better understand whether the S forms and AS activity always show permanent spatial patterns in soil or whether they are more randomized.

Geostatistical assessment of solid–liquid distribution coefficients (K d) for Cd, Cu, Pb and Zn in surface soils of Hamedan, Iran

This study has applied a geostatistical approach to analyzing and interpreting the distribution coefficient (K d) in soils collected around Hamedan, west of Iran. The samples collected from the agricultural area (about 100 ha) affected by waste water from Shahid Mofatteh power plant. Sorption experiments were carried out using solution of desired concentration of cadmium (Cd), copper (Cu), lead (Pb) and zinc (Zn) (10 and 100 mg l−1). Desorption experiments with CaCl2 0.01 M, were performed immediately following the completion of sorption experiments. Results showed that in all treatments K d100 values is lower than K d10 values. The range of K d10 (l kg−1) were as follows: Cd (253–1656), Cu (545–100000), Pb (1841–100000) and Zn (198–3115). Median K d values showed the following order of decreasing affinity in both 10 and 100 mg l−1 metal concentrations: Pb > Cu > Zn > Cd. Desorption analysis indicated that only trace amounts of heavy metal adsorbed was released. Irreversibility was highest for Pb and Cu, which the K d values were greatest. The resulting variograms of log-transformed K d10 data for Cu indicated the existence of strong spatial dependence. The variograms for K d10 Cd revealed moderate spatial structure and variograms for K d10 Pb and Zn show weak spatial dependence. For K d100, the variogram of Cd show weak spatial dependence and variograms of Cu, Pb and Zn indicate moderate spatial structure.

Mapping of Variability in Major and Micro Nutrients for Site-Specific Nutrient Management

Background: Spatial variation of soil physical and chemical properties influences soil and crop management efficiency causes uneven crop growth and decreases the effectiveness of uniformly applied fertilizers. Purpose: Therefore, a comprehensive survey was made to determine the spatial variability of soil properties and their mapping in Charsadda district of Khyber Pakhtunkhwa Province (KP) of Pakistan to delineated area into low, medium and high level of plant nutrients for site-specific nutrient management using variable rate fertilizer technology. Method: Soil sampling was done on a grid system using Global Position System (GPS) from two depths (0-15, and 15-45 cm) during 2004; and the samples were analyzed for soil physical (soil texture and saturation percentage), soil chemical (pH, ECe, SAR, lime, Original Research Article Khan et al.; IJPSS, Article no. IJPSS.2014.008 304 and organic matter) and soil fertility status (mineral N, AB-DTPA extractable P, K, Zn, Cu, Fe, Mn and HCl extractable B). Geostatistical techniques of semivariogram analysis and kriging were used to model the spatial variability and interpolation of data values at unsampled locations and mapping of the district. Semivariogram analyses of data showed some spatial patterns for soil properties. Silt (r2=0.48), clay (r2=0.71) contents and saturation percentage (r2=0.71) were described by linear model in both the depths (0-15, and 15-45 cm). Electrical conductivity was described by a linear model in both the depths with strong spatial structure in surface soil (r2=0.81). Calcium carbonate (CaCO3) in the surface soil had strong spatial structure (r2=0.59), organic matter content in the surface soil was described by a spherical model with a range of influence 6.65 km, while in the subsoil (15-45 cm) it was described by a linear model with moderate spatial structure (r2=0.41). Mineral N and P were described by linear models with strong spatial structure for P in both the depths (r2=0.77, 0.73) and moderate spatial structure (r2=0.36) for surface soil N. Potash content was described by a linear model in surface soil with moderate structure (r2=0.24), while in subsoil it was explained by a spherical model with strong spatial structure (r2=0.64) and a range of about 9 km. Zinc and Cu in the surface soil were randomly distributed, while they have strong spatial structure (r2=0.63 and 0.54, respectively) with a linear model in subsoil. Boron content in both the depths was described by a linear model with strong spatial structure in surface (r2=0.61) and moderate structure (r2=0.31) in subsoil. Results: The maps of various measured soil properties showed that soil mineral N and boron (B) increases from north-east toward west-south, available P form south-east towards north-west and lime form northern towards southern parts of the district. Soil organic matter, sand and silt contents showed little spatial variation within sampled areas. Conclusion: Texture of Charsadda district ranged from silt to sandy loam. Sand content in the east and silt in the whole area was higher, while clay was found low throughout the soil surveyed. All the soils were alkaline in reaction and calcareous in nature to different degrees as indicated in maps of surface soil pH and CaCO3. Organic matter content of both the depths was low. surface soils was deficient in N in all soils of district Charsadda while deficiencies of P, Zn and B were observed to a greater extent, while K, Cu and Mn are also appearing deficient in soil.

Spatial Variability of Nutrients in Wheat Plants in Semi-arid Regions of Northwestern Pakistan

This study was undertaken to assess spatial variability of nutrients in wheat plants for variable-rate site-specific fertilizer-management strategy. Wheat plants were collected from two semi-arid regions in northwestern Pakistan during 2004 and analyzed for nutrient contents and spatial variability. Wheat samples from both regions showed widespread deficiency of phosphorus (P), potassium (K), and zinc (Zn). In Kohat, Zn and manganese (Mn) were spatially distributed, described by linear models with moderate spatial patterns, but K, copper (Cu), and iron (Fe) showed weak spatial structures. In Bannu, K was described by a linear model with strong spatial structure and Fe had moderate spatial structure, but P, Cu, Mn, and Zn had random spatial distributions. The maps developed in this study showed variation in plant nutrients and spatial trends in the cases of spatially distributed nutrients and can be used to delineate plant nutrients into low, medium, and high categories to develop variable-rate fertilizer-management strategy.

Assessment of spatial variability of selected soil chemical properties in a communal irrigation scheme under resource-poor farming conditions in Vhembe District of Limpopo Province, South Africa

An understanding of spatial variability of chemical soil properties is necessary for proper nutrient management. Therefore spatial variabilities of soil pH, P, K, Ca and Mg in communal irrigation scheme under resource-poor farming conditions were determined. A total of 230 soil samples were collected from two soil depths (0 to 30 and 30 to 60 cm) at 100 m grid intervals. Basic statistics and geostatistical analyses of the data were performed using SAS 9.0 and GS+ 9, respectively. Soils showed high variabilities for all variables that were analyzed. Soil pH exhibited the lowest CV (6.0%) for both layers whereas all other measured variables displayed high CV for both soil layers. Most properties were analyzed by exponential model except for Ca and Mg that were fitted into spherical and Gaussian models respectively. All variables that were fitted into exponential model had strong spatial structure and those fitted into spherical model had a moderate spatial structure. Kriged contour maps displayed positional relationship between the topsoil and subsoil layers. Areas with low K and P can be delineated into separate management zones based on their requirements for these elements. The study showed that geostatistics is a useful tool to map spatial variabilities of soil chemical properties even under resource-poor farming conditions. These maps can be used to encourage/implement variable-rate of input application and inform resource-poor farmers of the benefits of this strategy, thereby reducing variation in soil fertility status caused by application of indiscriminate types and rates of manure and fertilizers. Key words: Site-specific soil management, soil layers, Kriged contour maps, geostatistics, exponential model, South Africa.

Planejamento amostral da pressão de preconsolidação de um latossolo vermelho distroférrico

Atualmente, a pressão de preconsolidação (sigmap) tem sido utilizada como uma ferramenta no estudo do processo de compactação do solo. O objetivo deste trabalho foi determinar a configuração e o número adequado de amostras para determinação da sigmap em um Latossolo Vermelho distroférrico, bem como caracterizar sua variabilidade e distribuição espacial, utilizando métodos da Estatística Clássica e da Geoestatística. O estudo foi realizado em uma área experimental, no município de Lavras-MG, sob plantio convencional nos últimos 30 anos, no qual são realizados anualmente uma aração e duas gradagens. A área experimental possui dimensões de 32 x 160 m (5.120 m²), com 68 pontos distanciados a 10 m no eixo X e a 8 m no eixo Y, mais seis transectos, sendo quatro com pontos distanciados de 2 m e dois com pontos distanciados de 1 m, visando detectar variações a pequenas distâncias. Foram coletadas 98 amostras indeformadas na profundidade de 0-3 cm, as quais foram submetidas ao ensaio de compressão uniaxial, para obtenção das sigmap. Por meio dos parâmetros da Estatística Clássica, estabeleceu-se o número adequado de amostras para determinar a sigmap, que foi igual a 10 pontos. A maior variabilidade foi obtida para a sigmap (CV 14,8 %), e a menor, para a umidade do solo (CV 12,0 %). Ambas as variáveis apresentaram distribuição normal, com modelo esférico ajustado, e moderada estrutura de dependência espacial, com alcance de 19,5 e 90,0 m, respectivamente. Em futuras amostragens para determinação da sigmap, em condições similares às da área estudada, sugere-se dispor os pontos de coleta com intervalo igual ao alcance de dependência espacial, visando associar menor esforço de amostragem com maior representatividade da área. Apesar de a razão de dependência espacial encontrada para as variáveis estudadas ter sido moderada, seu alcance deve ser considerado no planejamento de novas amostragens. A declividade do solo influenciou indiretamente os valores de sigmap. O mapeamento da área permitiu observar zonas de maior e menor suscetibilidade à compactação, possibilitando a tomada de decisão sobre onde começar a trafegar para evitar danos à estrutura do solo.

Significant differences in the length–weight relationships of neighbouring stocks can result in biased biomass estimates: Examples of haddock ( Melanogrammus aeglefinus, L.) and whiting ( Merlangius merlangus, L.)

Length–weight relationships of fish are often used to estimate the biomass of length distributions or to obtain indices of condition. Although large-scale spatial trends are known to exist, it is often assumed that length–weight relationships do not vary significantly within stocks or between neighbouring stocks. The present study examined length–weight relationships of 1334 haddock ( Melanogrammus aeglefinus) and 1186 whiting ( Merlangius merlangus) collected on a groundfish survey in the waters around Ireland in 2004. Additionally, condition indices were estimated for individual fish and for length-frequency samples in order to summarise the length–weight relationships. The length–weight regression showed a significant area effect but no significant differences existed between the sexes. The condition indices showed a moderate spatial structure for both species: around 25% of the variability could be explained by the location of the samples, the rest of the variability was due to other sources. Length–weight relationships did not appear to vary significantly within stocks, however differences between stocks were significant. In the present case, a bias of up to 10% could occur in biomass estimates as a result of applying length–weight relationships of one stock to length data of a neighbouring stock.

Spatial Structure in Drosophila buzzatii Populations: Simple and Directional Spatial Autocorrelation

The spatial structure of 12 allele frequencies was examined for 57 populations of the cactophilic fly Drosophila buzzatii from eastern Australia. Techniques include spatial-autocorrelation analysis and the newly developed directional spatial autocorrelation. Although 11 allele frequencies differ among localities, only 6 show spatial structure along one dimension. Directional correlograms support clines or major geographic trends in different directions for 5 allele frequencies. Spatial correlograms were also computed for genetic distances. The overall results-genetic heterogeneity among localities, weak correlation between allele-frequency surfaces, moderate spatial structure, and moderate parallelism of correlograms-permit elimination of genetic drift, selection against a single environmental gradient, or the effects of a single migrational event, and they suggest that selection operates on different spatial scales ranging from a continental one to a strictly local one. These results are interpreted in terms of the known history and biology of these organisms in Australia and are compared with spatial analyses of allele frequencies in other organisms.