Appropriateness of Management Zones for Characterizing Spatial Variability of Soil Properties and Irrigated Corn Yields across Years

Improvements in water productivity are of primary importance for maintaining agricultural productivity and sustainability. Water potential-based irrigation management has proven effective for this purpose with many different crops, including strawberries. However, problems related to spatial variability of soil properties and irrigation efficiency were reported when applying this management method to strawberries in soils with rock fragments. In this study, a field-scale experiment was performed to evaluate the impacts of three irrigation management scales and a pulsed water application method on strawberry yield and water productivity. An analytical solution to Richards’ equation was also used to establish critical soil water potentials for this crop and evaluate the effects of the variability in the soil properties. Results showed that spatial variability of soil properties at the experimental site was important but not enough to influence crop response to irrigation practices. The studied properties did not present any spatial structure that could allow establishing specific management zones. A four-fold reduction in the size of the irrigation management zones had no effect on yield and increased the water applications. Pulsed application led to significant yield (22%) and water productivity (36%) increases compared with the standard water application method used by the producer at the experimental site.

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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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A GIS-based approach to identify the spatial variability of salt affected soil properties and delineation of site-specific management zones: A case study from Egypt

Soil Science Annual ◽

10.37501/soilsa/121495 ◽

2020 ◽

Vol 71 (1) ◽

pp. 76-85

Author(s):

Mohamed Kamal Abdel-Fattah

Keyword(s):

Spatial Variability ◽

Soil Properties ◽

Site Specific ◽

Management Zones ◽

Salt Affected Soil ◽

Specific Management

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Spatial variability of apparent electrical conductivity and soil properties in a coffee production field

Engenharia Agrícola ◽

10.1590/s0100-69162014000600017 ◽

2014 ◽

Vol 34 (6) ◽

pp. 1224-1233 ◽

Cited By ~ 2

Author(s):

Domingos S. M. Valente ◽

Daniel M. de Queiroz ◽

Francisco de A. de C. Pinto ◽

Fábio L. Santos ◽

Nerilson T. Santos

Keyword(s):

Electrical Conductivity ◽

Spatial Variability ◽

Soil Properties ◽

Precision Agriculture ◽

Chemical Properties ◽

Coffee Production ◽

Management Zones ◽

Apparent Electrical Conductivity ◽

The Relationship ◽

Dense Sampling

Precision agriculture based on the physical and chemical properties of soil requires dense sampling to determine the spatial variability of these properties. This dense sampling is often expensive and time-consuming. One technique used to reduce sample numbers involves defining management zones based on information collected in the field. Some researchers have demonstrated the importance of soil electrical variables in defining management zones. The objective of this study was to evaluate the relationship between the spatial variability of the apparent electrical conductivity and the soil properties in the coffee production of mountain regions. Spatial variability maps were generated using a geostatistical method. Based on the spatial variability results, a correlation analysis, using bivariate Moran's index, was done to evaluate the relationship between the apparent electrical conductivity and soil properties. The maps of potassium (K) and remaining phosphorus (P-rem) were the closest to the spatial variability pattern of the apparent electrical conductivity.

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Spatial variability studies in São Paulo, Brazil along the last twenty five years

Bragantia ◽

10.1590/s0006-87052010000500007 ◽

2010 ◽

Vol 69 (suppl) ◽

pp. 53-66 ◽

Cited By ~ 5

Author(s):

Sidney Rosa Vieira ◽

Sonia Carmela Falci Dechen

Keyword(s):

Data Analysis ◽

Spatial Variability ◽

Physical Properties ◽

Soil Properties ◽

Crop Yields ◽

Soil Physical Properties ◽

Sao Paulo ◽

No Tillage ◽

Management Zones ◽

Sampling Points

Soil properties vary in space due to many causes. For this reason it is wise to know the magnitude and behaviour of the variability for adequate data analysis and decision making. Our work on spatial variability of soil properties in São Paulo, Brazil began in 1982 with a very simple soil sampling in a small field. Much progress has been made since then on sampling designs, field equipment and methods, and mostly on computation equipment and softwares. This paper reports the results corresponding to some aspects of this progress, as far as the field, analysis and computation work are concerned. The objective of this study was to illustrate the use of geostatistics in data analysis for three sampling conditions on long term no-tillage system. The analysis is done on a wide range of field scales, variables, sampling schemes as well as repeating sampling scheme for the same variable in different years. Semivariograms are compared for the same variables in different scales and sampling dates and depths as to provide a guide for sampling spacing and number of samples. Normalized crop yield parameters for many years are used in the discussion of time variability and on the use of yield maps to locate management zones. The time of the year in which measurements of soil physical properties are made affected the results both in terms of descriptive statistical and spatial dependence parameters. Crop yields changed (soybean decrease and maize increase) with time of no-tillage but the real cause was not identified. The length of time with no-tillage affected the range of dependence for the main crops (increased for soybean, maize and oats) and therefore increased the size of the homogeneous management zones. The evolution of the sampling grid from 20 m with 63 sampling points to 10 m with 302 sampling points allowed for a much better knowledge of the spatial variability of crop yields but it had the reverse effect on the spatial variability of soil physical properties.

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Spatial variability of some chemical properties of a Cambisol soil with cocoa (Theobroma cacao L.) cultivation

RECUBRIMIENTO COMESTIBLE CON BASE DE PIÑON MEXICANO (Jatropha curcas) SOBRE LA CALIDAD DE CHAYOTE MINIMAMENTE PROCESADO ◽

10.32854/agrop.v14i1.1706 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Sergio Salgado García ◽

Joana Acopa Colorado ◽

Sergio Salgado-Velázquez ◽

Samuel Córdova Sánchez ◽

David Palma López ◽

...

Keyword(s):

Spatial Variability ◽

Soil Properties ◽

Spatial Dependence ◽

Chemical Properties ◽

Kriging Interpolation ◽

Management Zones ◽

Agronomic Management ◽

Management Area ◽

Theobroma Cacao L ◽

Exchangeable P

Objective: To evaluate the spatial variability of some chemical properties of a Cambisolsoil, in order to establish specific agronomic management zones for cocoa cultivation.Methodology: A sampling of 42 georeferenced points equidistant at 40 m was carriedout. Geostatistical variability maps were made with the results of the chemical analysisof the soil properties, using the ordinary Kriging interpolation technique.Results: It was found that the percentage of saturation of acidity (PSA), acidity and H+showed high variability; P-Olsen and interchangeable K, Ca and Mg displayed mediumvariability, and pH, MO, CIC and Al presented low variability. Soil properties pH, PSA;Exchangeable P-Olsen, Ca and Mg showed high spatial dependence (<25%) and OM,exchangeable K and CIC moderate spatial dependence (25-75%).Study limitations / Implications: The generated maps allowed the identification ofpartial areas with different variability, as well as the direction of greatest variability of theproperty as a function of distance.Conclusions: With the maps, it was possible to make recommendations for agronomicmanagement depending on each specific management area.

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