Spatial variability and correlation of soil properties in the Annaba plain (North-East Algeria)

2018 ◽  
Vol 19 (3) ◽  
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
North East

scholarly journals Predictive Mapping of Soil Properties for Precision Agriculture Using Geographic Information System (GIS) Based Geostatistics Models

2019 ◽  
Vol 13 (10) ◽  
pp. 60 ◽  
Author(s):  
John Kingsley ◽  
Solomon Odafe Lawani ◽  
Ayito Okon Esther ◽  
Kebonye Michael Ndiye ◽  
Ogeh Joseph Sunday ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Precision Agriculture ◽  
Soil Analysis ◽  
Soil Samples ◽  
Soil Parameters ◽  
Available Phosphorus ◽  
Predictive Tool ◽  
Large Variability ◽  
North East

In precision Agriculture, geostatistical methods as a predictive tool have been extensively utilized. The approach estimates soil properties spatial variability and dependency. This study was carried out in Ovia north east Local Government Area of Edo State of Nigeria in order to map soil properties (Sand, Clay, pH, OC, P, N and CEC) and redict their spatial variability. Twenty-nine (29) soil samples were collected randomly from Typic Kandiudults soil type under three different land use, teak forest plantation, shrub, and arable farm. The soil samples were air-dried and passed through a 2 mm sieve before being analyzed for pH(CaCl2), SOC, Sand, Clay, Phosphorus, Nitrogen, and CEC. Generated data were statistically and geostatistically computed to explain the spatial variability of soil properties. The traditional method of soil analysis and interpretation are tedious, time-consuming with escalating budgets thus geostatical approach. Available phosphorus yielded large variability with CV=57.08% followed by clay content with CV=49.03%. Spherical, Gaussian, Hole Effect model, Stable, Exponential and Circular models were fitted for all the soil parameters. The result revealed that soil pH, Sand content, TN and CEC were moderate spatially autocorrelated with nugget/sill value of 0.32, 0.21, 0.49 and 0.30 respectively.  SOC also gave a moderate spatially autocorrelated with nugget/sill value of 0.44. And Clay and Available phosphorus were strong spatially autocorrelated with nugget/sill value of 0.15 and 0.13 respectively. Cross-validation of the output maps using the semivariogram showed that the interpolation models are superior to assuming mean for any unsampled area. The output maps will help soil users within the area to proffer best management technology to improve crop, fiber and water production.   

Effect of Slope Mesorelief on the Spatial Variability of Soil Properties and Vegetation Index Based on Remote Sensing Data

2019 ◽  
Vol 55 (9) ◽  
pp. 1329-1337
Author(s):  
N. V. Gopp ◽  
T. V. Nechaeva ◽  
O. A. Savenkov ◽  
N. V. Smirnova ◽  
V. V. Smirnov
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Soil Properties ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Sensing Data

Spatial Variability of Rainfed Wheat Production Under the Influence of Topography and Soil Properties in Loess-Derived Soils, Northern Iran

2020 ◽  
Vol 14 (4) ◽  
pp. 597-608
Author(s):  
Mohammad Ajami ◽  
Ahmad Heidari ◽  
Farhad Khormali ◽  
Mojtaba Zeraatpisheh ◽  
Manouchehr Gorji ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Northern Iran ◽  
Wheat Production ◽  
Rainfed Wheat

scholarly journals Spatial variability in soil properties on slow-forming terraces in the Andes region of Ecuador

2003 ◽  
Vol 72 (1) ◽  
pp. 31-41 ◽  
Author(s):  
Gerd Dercon ◽  
Jozef Deckers ◽  
Gerard Govers ◽  
Jean Poesen ◽  
Henrry Sánchez ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
The Andes ◽  
Andes Region

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.

scholarly journals Regionalized Terrestrial Ecotoxicity Assessment of Copper-Based Fungicides Applied in Viticulture

2018 ◽  
Vol 10 (7) ◽  
pp. 2522 ◽  
Author(s):  
Ivan Viveros Santos ◽  
Cécile Bulle ◽  
Annie Levasseur ◽  
Louise Deschênes
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Regression Models ◽  
Management Practices ◽  
Metal Speciation ◽  
Agricultural Systems ◽  
Geographical Variability ◽  
Terrestrial Ecotoxicity ◽  
The Impact ◽  
Growing Region

Life cycle assessment has been recognized as an important decision-making tool to improve the environmental performance of agricultural systems. Still, there are certain modelling issues related to the assessment of their impacts. The first is linked to the assessment of the metal terrestrial ecotoxicity impact, for which metal speciation in soil is disregarded. In fact, emissions of metals in agricultural systems contribute significantly to the ecotoxic impact, as do copper-based fungicides applied in viticulture to combat downy mildew. Another issue is linked to the ways in which the intrinsic geographical variability of agriculture resulting from the variation of management practices, soil properties, and climate is addressed. The aim of this study is to assess the spatial variability of the terrestrial ecotoxicity impact of copper-based fungicides applied in European vineyards, accounting for both geographical variability in terms of agricultural practice and copper speciation in soil. This first entails the development of regionalized characterization factors (CFs) for the copper used in viticulture and then the application of these CFs to a regionalized life-cycle inventory that considers different management practices, soil properties, and climates in different regions, namely Languedoc-Roussillon (France), Minho (Portugal), Tuscany (Italy), and Galicia (Spain). There are two modelling alternatives to determine metal speciation in terrestrial ecotoxicity: (a) empirical regression models; and (b) WHAM 6.0, the geochemical speciation model applied according to the soil properties of the Harmonized World Soil Database (HWSD). Both approaches were used to compute and compare regionalized CFs with each other and with current IMPACT 2002+ CF. The CFs were then aggregated at different spatial resolutions—global, Europe, country, and wine-growing region—to assess the uncertainty related to spatial variability at the different scales and applied in the regionalized case study. The global CF computed for copper terrestrial ecotoxicity is around 3.5 orders of magnitude lower than the one from IMPACT 2002+, demonstrating the impact of including metal speciation. For both methods, an increase in the spatial resolution of the CFs translated into a decrease in the spatial variability of the CFs. With the exception of the aggregated CF for Portugal (Minho) at the country level, all the aggregated CFs derived from empirical regression models are greater than the ones derived from the method based on WHAM 6.0 within a range of 0.2 to 1.2 orders of magnitude. Furthermore, CFs calculated with empirical regression models exhibited a greater spatial variability with respect to the CFs derived from WHAM 6.0. The ranking of the impact scores of the analyzed scenarios was mainly determined by the amount of copper applied in each wine-growing region. However, finer spatial resolutions led to an impact score with lower uncertainty.

Spatial Variability of Soil Properties, Cotton Yield, and Quality in a Production Field

2007 ◽  
Vol 39 (1-2) ◽  
pp. 1-16 ◽  
Author(s):  
J. L. Ping ◽  
C. J. Green ◽  
R. E. Zartman ◽  
K. F. Bronson ◽  
T. F. Morris
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Cotton Yield ◽  
Yield And Quality
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