Delineation of soil management zone using geostatistical and fuzzy approaches based on soil properties and wheat yield (a case study in Dashte-Naz, Sari)

Articles in Press

Document Type : Complete scientific research article

Authors

1 Department of Soil Science and Engineering, Faculty of Crop Sciences, Sari Agricultural Sciences and Natural Resources University

2 Department of Soil Sciences and Engineering, Faculty of Crop Sciences, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

10.22069/ejsms.2026.23599.2192

Abstract

Abstract
Background and aims: Efficient and cost-effective methods for managing agricultural fields are currently needed to maximize economic benefits and minimize the environmental impacts. Fertilizer recommendations in Iran are mostly apply as an uniform pattern across the fields, leading to over-application in nutrient-rich zones and under-application in nutrient-deficient zones. This necessitates the efficient techniques for accurately measuring spatial variations of soil properties and the identifying homogeneous management zones for optimization of fertilizer application.
Materials and Methods: In order to delineate the soil management zone and evaluate the spatial variability of soil properties a field in Dashte-Naz area, Sari city in Iran with 44.5 ha was selected by integration of geostatistics, principal component analysis (PCA) and fuzzy clustering algorithm. The 108 soil samples were collected from a grid dimensions of 60 m× 60 m from a depth of 0-30 cm. Soil properties including pH, EC, organic carbon, soil, N-nitrate, phosphorus, potassium, cation exchange capacity and yield of wheat were determined. Spatial variability of soil properties was performed by geostatistical analysis. In order to estimate the soil properties in unsampled location the kriging and inverse distance weighting (IDW with powers of 1, 2, 3) interpolation methods were used. The statistical indicators of RMSE and ME was used to select the best interpolation method.
Results: The best interpolation method for pH, EC, organic carbon were IDW with power of 2 and 1, and kriging, respectively. The best interpolation method for CEC was IDW with power of 1 and for other soil properties the kriging method had best interpolation results. The map of soil properties with the resolution of 15 m interval was prepared with the best identified interpolation methods at the field. The PCA was done on all interpolated points and then the fuzzy clustering algorithm with MZA software was used to identify the homogenous management zones. The fuzziness performance index (FPI) and normalized classification entropy (NEC) was used to determine the optimal number of clusters. The study area was divided into three management zones, with 20.5 hectares located in Zone 1, 14.9 hectares in Zone 2, and 9.02 hectares in Zone 3. Additionally, Zone 3 exhibited better fertility and crop performance quality. In contrast, Zones 1 and 2, due to their lower fertility levels, required higher fertilizer application to achieve the optimal productivity.
Conclusion: The overall results indicated that the optimal number of management zones for the study area was three. Analysis of variance (ANOVA) revealed the significant heterogeneity in soil fertility properties. Therefore, when these results are applied to the target crop according to the management zones, they can prevent uniform fertilizer application. This approach not only optimizes agricultural costs, but also prevents soil resource degradation and maximizes crop production.

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Main Subjects

References

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Journal of Soil Management and Sustainable Production

Articles in Press, Accepted Manuscript
Available Online from 15 February 2026

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