scholarly journals Deciphering Soil Spatial Variability through Geostatistics and Interpolation Techniques

2020 ◽  
Vol 13 (1) ◽  
pp. 194
Author(s):  
Mohamed A. E. AbdelRahman ◽  
Yasser M. Zakarya ◽  
Mohamed M. Metwaly ◽  
Georgios Koubouris
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Precision Agriculture ◽  
Nutrient Management ◽  
Prediction Method ◽  
Available Phosphorus ◽  
Detailed Knowledge ◽  
Agricultural Planning ◽  
Management Recommendations ◽  
Soil Spatial Variability

Detailed knowledge of soil properties is fundamentally important for optimizing agriculture practices and management. Meanwhile, the spatial distribution of soil physicochemical properties is considered a fundamental input of any sustainable agricultural planning. In the present study, ordinary kriging, regression kriging and IDW were chosen for deciphering soil spatial variability and mapping soil properties in a reclaimed area of the Behera Governorate of Egypt where soil arose from two different types, one sandstone and the other limestone. Geostatistics were used to show the interrelationships and conditions of soil properties (available phosphorus, potassium and nitrogen, EC, pH, Sp, ESP, CEC, OC, SAR, and CaCO3). The results of mapping spatial soil variability by Geostatistics could be used for precision agriculture applications. Based on the soil test results, nutrient management recommendations should be applied regarding variable rates of fertilizers. The performance of the maps was evaluated using Mean square error (MSE). Inverse distance weight (IDW) showed higher efficiency than Kriging as a prediction method for mapping the studied soil properties in the study area. The results of the present study suggest that the application of the selected fit model worldwide in any relevant study of soil properties of different geological sources is feasible.

scholarly journals Soil Properties Spatial Variability and Delineation of Site-Specific Management Zones Based on Soil Fertility Using Fuzzy Clustering in a Hilly Field in Jianyang, Sichuan, China

2019 ◽  
Vol 11 (24) ◽  
pp. 7084 ◽  
Author(s):  
Mohamed S. Metwally ◽  
Sameh M. Shaddad ◽  
Manqiang Liu ◽  
Rong-Jiang Yao ◽  
Ahmed I. Abdo ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Soil Degradation ◽  
Crop Production ◽  
Nutrient Management ◽  
Soil Nutrient ◽  
Available Phosphorus ◽  
Site Specific ◽  
Management Zones ◽  
Nutrient Contents

Avoiding soil degradation and improving crop productivity could be achieved by performing sustainable soil nutrient management with an appropriate understanding of soil properties’ spatial variability. The present fertilizer recommendations for the region where the study area is located are typically symmetric for large regions. This leads to the under-application of fertilizers in zones with low nutrient contents and over-application in zones with high nutrient contents. Therefore, this study was conducted to assess soil management zones (MZs) in the study area for effective soil nutrient management and to evaluate soil properties’ spatial variability. A total of 100 geo-referenced soil samples were collected at a depth of 0–20 cm, processed and analyzed for pH, available nitrogen (AN), available phosphorus (AP), available potassium (AK), soil organic carbon (SOC), total nitrogen (TN) and total phosphorous (TP), while C:N, C:P and N:P ratios were calculated. Soil properties’ coefficients of variation (CVs) widely varied from low (1.132%) to moderate (45.748%). Ordinary kriging and semi-variogram analysis showed differed spatial variability patterns for the studied soil properties with spatial dependence ranged from weak to strong. MZs were delineated by performing principal component analysis (PCA) and fuzzy K-means clustering. Four PCs with eigen values more than 1 dominated 84.44% of the total variance, so they were retained for clustering analysis. Three MZs were delineated based on the two criteria modified partition entropy (MPE) and fuzzy performance index (FPI). The studied soil properties differed significantly among MZs. Thus, the methodology used for MZ delineation could be used effectively for soil site-specific nutrient management for avoiding soil degradation concurrently with maximizing crop production in the study area.

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.   

scholarly journals Temporal stability of electrical conductivity in a sandy soil

2016 ◽  
Vol 30 (3) ◽  
pp. 349-357 ◽  
Author(s):  
Aura Pedrera-Parrilla ◽  
Eric C. Brevik ◽  
Juan V. Giráldez ◽  
Karl Vanderlinden
Keyword(s):  
Electrical Conductivity ◽  
Spatial Variability ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Precision Agriculture ◽  
Temporal Stability ◽  
Principal Component ◽  
Model Parameters ◽  
Soil Spatial Variability

Abstract Understanding of soil spatial variability is needed to delimit areas for precision agriculture. Electromagnetic induction sensors which measure the soil apparent electrical conductivity reflect soil spatial variability. The objectives of this work were to see if a temporally stable component could be found in electrical conductivity, and to see if temporal stability information acquired from several electrical conductivity surveys could be used to better interpret the results of concurrent surveys of electrical conductivity and soil water content. The experimental work was performed in a commercial rainfed olive grove of 6.7 ha in the ‘La Manga’ catchment in SW Spain. Several soil surveys provided gravimetric soil water content and electrical conductivity data. Soil electrical conductivity values were used to spatially delimit three areas in the grove, based on the first principal component, which represented the time-stable dominant spatial electrical conductivity pattern and explained 86% of the total electrical conductivity variance. Significant differences in clay, stone and soil water contents were detected between the three areas. Relationships between electrical conductivity and soil water content were modelled with an exponential model. Parameters from the model showed a strong effect of the first principal component on the relationship between soil water content and electrical conductivity. Overall temporal stability of electrical conductivity reflects soil properties and manifests itself in spatial patterns of soil water content.

Assessment of spatial variability of soil properties using geospatial techniques for farm level nutrient management

2017 ◽  
Vol 169 ◽  
pp. 25-34 ◽  
Author(s):  
Duraisamy Vasu ◽  
S.K. Singh ◽  
Nisha Sahu ◽  
Pramod Tiwary ◽  
P. Chandran ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Nutrient Management ◽  
Geospatial Techniques ◽  
Farm Level

Mapping of spatial variability in soil properties for site-specific nutrient management of Nagpur Mandarin in Central India

2018 ◽  
Vol 75 (2) ◽  
pp. 209 ◽  
Author(s):  
S.S. Sawant ◽  
M.S.S. Nagaraju ◽  
Rajeev Srivastava ◽  
Jagdish Prasad ◽  
R.A. Nasre ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Nutrient Management ◽  
Central India ◽  
Site Specific

scholarly journals Effects of integrated nutrient management in early season cauliflower production and its residual effects on soil properties

2020 ◽  
Vol 3 (2) ◽  
pp. 353-365
Author(s):  
Babita Neupane ◽  
Krishna Aryal ◽  
Lal Bahadur Chhetri ◽  
Shishir Regmi
Keyword(s):  
Soil Properties ◽  
Nutrient Management ◽  
Particle Density ◽  
Organic Matter Content ◽  
Poultry Manure ◽  
Matter Content ◽  
Growth And Yield ◽  
Residual Effects ◽  
Available Phosphorus ◽  
Integrated Nutrient Management

This experiment was conducted in the farmer’s field at Khajrauta, Gadhawa-4, Dang, Nepal to evaluate the effect of integrated nutrient management on growth and yield of cauliflower as well as their residual effects on soil properties. The cauliflower variety silvercup-60 was grown under eight different treatments; T1: 50% N through RDF + 50% N through FYM; T2: 50% N through RDF + 50% N through PM; T3: 50% N through RDF + 50% N through VC, T4: 50% N through RDF + 25% N through FYM + 25% N through PM; T5: 50% N through RDF + 25% N through VC + 25% N through PM; T6: 50% N through RDF + 25% N through VC + 25% N through FYM; T7: 50% N through RDF + 25% N through  VC +25% N through FYM; T8: 50% N through RDF + 50% N  through FYM,VC and poultry manure. The experiment was laid out in RCB design with three replications. The result revealed that the  highest plant height (36.40 cm), number of leaves (15), plant spread (31.72 cm), leaf area (526.5 cm2), curd weight (207.3g) and curd yield (12.85 t/ha) were found under 50% N through RDF +50% N through VC. The root length, root diameter and root density were better under all INM treatments as compared to 100% N through RDF. INM treatments showed lesser bulk density, lesser particle density, greater infiltration rate and greater organic matter content than application of 100% N through RDF. Soil total nitrogen was increased in all INM treatments while soil available phosphorus decreases in all treatments except 100% N trough RDF and 50% N through RDF +50% N through PM. Thus, farmers are suggested to apply 50% N through VC along with 50% N through RDF to increase cauliflower yield.   

scholarly journals Spatial Variability and Geostatistical Analysis of Soil Properties in the Diversified Cropping Regions of Bangladesh Using Geographic Information System Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Md. Zulfikar Khan ◽  
Md. Rafikul Islam ◽  
Ahmed Bin Abdus Salam ◽  
Tama Ray
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Nutrient Management ◽  
Sampling Site ◽  
Geostatistical Analysis ◽  
Kriging Interpolation ◽  
Total N ◽  
Study Region ◽  
Site Specific ◽  
Classical Statistics

Mapping of soil properties is an important operation as it plays an important role in the knowledge about soil properties and how it can be used sustainably. The study was carried out in a local government area in Bangladesh in order to map out some soil properties and assess their variability within the area. From the study area, a total of 92 soil samples (0–20 cm) were collected from different cropping patterns at an interval of 2.2 × 2.2 km2 on a regular grid design. A portable global positioning system (GPS) was used to collect coordinates of each sampling site. Then, soil properties, that is, pH, electrical conductivity (EC), soil organic carbon (SOC), total nitrogen (Total N), and soil available nutrients (P, K, and S) were measured in the laboratory. After the normalization of data, classical statistics were used to describe the soil properties, and geostatistical analysis was used to illustrate the spatial variability of the soil properties by using kriging interpolation techniques in a GIS environment. Results show that the spatial distribution and spatial dependency level of soil properties can be different even within the small or large scale. According to cross-validation results, for most soil properties, the kriging interpolation method provided the least interpolation error. The generated maps of soil properties that indicate soil nutrient status over the study region could be helpful for farmers and decision-makers to enhance site-specific nutrient management. Also, these prototype maps would be helpful for future nutrient and fertilizer applications management, including a site-specific condition to not only reduce the cost of input management but also prevent any environmental hazard. It also demonstrates that the effectiveness of geostatistics and GIS techniques provided a powerful tool for this study in terms of regionalized nutrient management.

Characterizing Soil Spatial Variability for Precision Agriculture Using Geophysical Measurements

2004 ◽  
Author(s):  
Dennis L. Corwin ◽  
Scott M. Lesch ◽  
Peter J. Shouse ◽  
Richard Soppe ◽  
James E. Ayars
Keyword(s):  
Spatial Variability ◽  
Precision Agriculture ◽  
Soil Spatial Variability ◽  
Geophysical Measurements
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