Spatial Variability of Measured Soil Properties across Site-Specific Management Zones

SUMMARYDelineating site-specific management zones within fields can be helpful in addressing spatial variability effects for adopting precision farming practices. A 3-year (2008/09 to 2010/11) field study was conducted at the Postgraduate Agricultural Research Station, University of Agriculture, Faisalabad, Pakistan, to identify the most important soil and landscape attributes influencing wheat grain yield, which can be used for delineating management zones. A total of 48 soil samples were collected from the top 300 mm of soil in 8-ha experimental field divided into regular grids of 24 × 67 m prior to sowing wheat. Soil and landscape attributes such as elevation, % of sand, silt and clay by volume, soil electrical conductivity (EC), pH, soil nitrogen (N) and soil phosphorus (P) were included in the analysis. Artificial neural network (ANN) analysis showed that % sand, % clay, elevation, soil N and soil EC were important variables for delineating management zones. Different management zone schemes ranging from three to six were developed and evaluated based on performance indicators using Management Zone Analyst (MZA V0·1) software. The fuzziness performance index (FPI) and normalized classification entropy NCE indices showed minimum values for a four management zone scheme, indicating its appropriateness for the experimental field. The coefficient of variation values of soil and landscape attributes decreased for each management zone within the four management zone scheme compared to the entire field, which showed improved homogeneity. The evaluation of the four management zone scheme using normalized wheat grain yield data showed distinct means for each management zone, verifying spatial variability effects and the need for its management. The results indicated that the approach based on ANN and MZA software analysis can be helpful in delineating management zones within the field, to promote precision farming practices effectively.

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Delineation of management zones with measurements of soil apparent electrical conductivity in the southeastern pampas

Canadian Journal of Soil Science ◽

10.4141/cjss2012-022 ◽

2013 ◽

Vol 93 (2) ◽

pp. 205-218 ◽

Cited By ~ 27

Author(s):

Nahuel Raúl Peralta ◽

José Luis Costa ◽

Mónica Balzarini ◽

Hernán Angelini

Keyword(s):

Electrical Conductivity ◽

Soil Properties ◽

Stepwise Regression ◽

Clay Soil ◽

Crop Yields ◽

Management Strategies ◽

Site Specific ◽

Management Zones ◽

Apparent Electrical Conductivity ◽

Specific Management

Peralta, N. R., Costa, J. L., Balzarini, M. and Angelini, H. 2013. Delineation of management zones with measurements of soil apparent electrical conductivity in the southeastern pampas. Can. J. Soil Sci. 93: 205–218. Site-specific management demands the identification of subfield regions with homogeneous characteristics (management zones). However, determination of subfield areas is difficult because of complex correlations and spatial variability of soil properties responsible for variations in crop yields within the field. We evaluated whether apparent electrical conductivity (ECa) is a potential estimator of soil properties, and a tool for the delimitation of homogeneous zones. ECamapping of a total of 647 ha was performed in four sites of Argentinean pampas, with two fields per site composed of several soil series. Soil properties and ECawere analyzed using principal components (PC)–stepwise regression and ANOVA. The PC–stepwise regression showed that clay, soil organic matter (SOM), cation exchange capacity (CEC) and soil gravimetric water content (θg) are key loading factors, for explaining the ECa(R2≥0.50). In contrast, silt, sand, extract electrical conductivity (ECext), pH values and [Formula: see text]-N content were not able to explain the ECa. The ANOVA showed that ECameasurements successfully delimited three homogeneous soil zones associated with spatial distribution of clay, soil moisture, CEC, SOM content and pH. These results suggest that field-scale ECamaps have the potential to design sampling zones to implement site-specific management strategies.

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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

Sustainability ◽

10.3390/su11247084 ◽

2019 ◽

Vol 11 (24) ◽

pp. 7084 ◽

Cited By ~ 7

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.

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Establishing management zones using soil electrical conductivity and other soil properties by the fuzzy clustering technique

Scientia Agricola ◽

10.1590/s0103-90162008000600001 ◽

2008 ◽

Vol 65 (6) ◽

pp. 567-573 ◽

Cited By ~ 23

Author(s):

José Paulo Molin ◽

Cesar Nunes de Castro

Keyword(s):

Electrical Conductivity ◽

Soil Fertility ◽

Soil Properties ◽

Precision Agriculture ◽

Principal Component ◽

Site Specific ◽

Management Zones ◽

Physical Attributes ◽

Definition Of ◽

Specific Management

The design of site-specific management zones that can successfully define uniform regions of soil fertility attributes that are of importance to crop growth is one of the most challenging steps in precision agriculture. One important method of so proceeding is based solely on crop yield stability using information from yield maps; however, it is possible to accomplish this using soil information. In this study the soil was sampled for electrical conductivity and eleven other soil properties, aiming to define uniform site-specific management zones in relation to these variables. Principal component analysis was used to group variables and fuzzy logic classification was used for clustering the transformed variables. The importance of electrical conductivity in this process was evaluated based on its correlation with soil fertility and physical attributes. The results confirmed the utility of electrical conductivity in the definition of management zones and the feasibility of the proposed method.

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Identificación de zonas de manejo sitio-específico a partir de la combinación de variables de suelo

Ciencia y Tecnología Agropecuaria ◽

10.21930/rcta.vol13_num1_art:239 ◽

2012 ◽

Vol 13 (1) ◽

pp. 47 ◽

Cited By ~ 2

Author(s):

Mariano Córdoba ◽

Mónica Balzarini ◽

Cecilia Bruno ◽

José Luis Costa

Keyword(s):

Spatial Variability ◽

Principal Component ◽

Production Potential ◽

Site Specific ◽

Management Zones ◽

Soil Variables ◽

Regionalized Variables ◽

Several Variables ◽

Specific Management

El manejo sitio-específico demanda la identificación de sub-regiones homogéneas, o zonas de manejo (ZM), dentro del espacio productivo. Sin embargo, definir ZM suele ser complejo debido a que la variabilidad espacial del suelo puede depender de varias variables. La zonificación o delimitación de ZM puede realizarse utilizando una variable de suelo a la vez o considerando varias variables simultáneamente. Entre los métodos de análisis multivariado, difundido para la zonificación, se encuentra el análisis de conglomerados fuzzy k-means (KM) y el análisis de componentes principales (PCA). No obstante, como otros métodos multivariados, éstos no han sido desarrollados específicamente para datos georreferenciados. Una nueva versión del PCA, conocido como MULTISPATI-PCA (PCAe), permite contemplar la autocorrelación espacial entre datos de variables regionalizadas. El objetivo de este estudio fue proponer una nueva estrategia de análisis para la identificación de ZM, combinando la aplicación KM y PCAe sobre datos de múltiples variables de suelo. La capacidad del método propuesto se evaluó en base a la comparación de los rendimientos promedios alcanzados en cada zona delimitada, tanto para la combinación de KM con PCA, la aplicación tradicional de KM sobre las variables originales y la nueva propuesta KM-PCAe. Los resultados mostraron que KM-PCAe fue el único método que permitió distinguir zonas estadísticamente diferentes en cuanto al potencial productivo. Se concluye que la combinación propuesta constituye una herramienta importante para el mapeo de la variabilidad espacial y la identificación de ZM a partir de datos georreferenciados. Identification of site-specific management zones from combination of soil variablesSite-specific management demands the identification of homogeneous subfield regions within the field or management zones (MZ). However, due to the spatial variability of soil variables, determination of MZ from several variables, is often complex. Although the zonification or delimitation of MZ may be univariate, it is more appropriate to consider all variables simultaneously. Fuzzy k-means clustering (KM) and principal component analysis (PCA) are multivariate analyses that have been used for zonification. Nevertheless, PCA and KM have not been explicitly developed for georeferenced data. Novel versions of PCA, known as MULTISPATI-PCA (PCAe), incorporate spatial autocorrelation among data of neighbor sites of regionalized variables. The objective of this study was to propose a new analytical tool to identify homogeneous zones from the combination of KM and PCAe on multiple soil variable data. The performance of proposed method was assessed through comparison of the average yields obtained in each zone delimited by combination of KM with PCA, as well as KM on the original variables and the new proposed method KM-PCAe. The results showed that KM-PCAe was the only method able to identify zones statistically different in terms of production potential. PCAe and its combination with KM are useful tools to map spatial variability and to identify MZ within fields from georeferenced data.

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Delineation of Site-Specific Management Zones Based on Temporal and Spatial Variability of Soil Electrical Conductivity

Pedosphere ◽

10.1016/s1002-0160(07)60021-6 ◽

2007 ◽

Vol 17 (2) ◽

pp. 156-164 ◽

Cited By ~ 24

Author(s):

Yan LI ◽

Zhou SHI ◽

Feng LI

Keyword(s):

Electrical Conductivity ◽

Spatial Variability ◽

Soil Electrical Conductivity ◽

Site Specific ◽

Management Zones ◽

Temporal And Spatial Variability ◽

Temporal And Spatial ◽

Specific Management

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Assessing soil spatial variability and delineating site-specific management zones for a coastal saline land in eastern India

Archives of Agronomy and Soil Science ◽

10.1080/03650340.2019.1578345 ◽

2019 ◽

Vol 65 (13) ◽

pp. 1775-1787 ◽

Cited By ~ 1

Author(s):

Tripathi Rahul ◽

Nayak Amaresh Kumar ◽

Dhal Biswaranjan ◽

Shahid Mohammad ◽

Lal Banwari ◽

...

Keyword(s):

Spatial Variability ◽

Eastern India ◽

Site Specific ◽

Management Zones ◽

Saline Land ◽

Soil Spatial Variability ◽

Specific Management

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DELINEATION OF SITE-SPECIFIC MANAGEMENT ZONES USING MULTIVARIATE ANALYSIS AND GEOGRAPHIC INFORMATION SYSTEM TECHNIQUE

PLANT ARCHIVES ◽

10.51470/plantarchives.2021.v21.no1.185 ◽

2021 ◽

Vol 21 (No 1) ◽

Author(s):

Basma S. Amer ◽

Karam F. Moussa ◽

Adel A. Sheha ◽

Mohamed K. Abdel- Fattah

Keyword(s):

Cluster Analysis ◽

Soil Properties ◽

Soil Characteristics ◽

Exchange Capacity ◽

Available N ◽

Site Specific ◽

Management Zones ◽

Geographic Information System Technique ◽

And Cluster Analysis ◽

Specific Management

The basic objectives of this study were: 1) The ability to characterize the spatial variability across a soil for selected soil properties using GIS technique and 2) identification of site-specific management zones using selected soil properties using PCA and cluster analysis. 120 geo-referenced representative soil samples obtained from Sahl AlHussainiyah, El-Sharkia Governorate, Egypt, (from 0 to 0.60 m depth). These samples were prepared and analysed for soil characteristics, such as soil pH, electrical conductivity (ECe), calcium carbonate (CaCO3 ), soil organic matter (OM), available N, P, K, soil cations exchange capacity (CEC), and bulk density (BD). Using semi-variogram analysis and ordinary kriging, spatial distribution pattern varies from moderate to strong spatial dependence for most soil characteristics. Using PCA and cluster analysis, site-specific management zones were identified in the study area. For further analysis, four PCs with eigenvalues > 1 were used, with PCs explaining 73.19 percent of the variance. Four MZs were defined based on cluster analysis using an agglomerative hierarchical clustering technique. The differences between these MZs were statistically significant (p < 0.05). 3.01 (466.56 ha), 36.47 (5658.9 ha), 31.02 (4813.54 ha), and 29.5 percent (4577.8 ha) of the total area is MZ1, MZ2, MZ3, and MZ4 (15516.8 ha).

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