Optimization of Management Zone Delineation for Precision Crop Management in an Intensive Farming System

Area of Science:

• Agricultural Science
• Soil Science
• Data Science

Background:

• Soil properties exhibit significant spatiotemporal variability, impacting agricultural productivity.
• Management zones (MZs) are essential for addressing spatial variability, but traditional clustering methods lack spatial dependence consideration.
• Existing methods for delineating MZs, such as K-means and fuzzy C-means, have limitations in accuracy due to ignoring spatial autocorrelation.

Purpose of the Study:

• To optimize the delineation of management zones (MZs) by evaluating novel variable reduction and clustering techniques.
• To improve the accuracy of MZ delineation by incorporating spatial dependence of soil variables.
• To provide guidance for precise nutrient management in intensive agricultural systems.

Main Methods:

• Six soil variables (pH, N, OM, P, K, EC) were analyzed for spatial variability.
• Variable reduction techniques, including Principal Component Analysis (PCA) and Multivariate Spatial Analysis (MULTISPATI-PCA), were employed.
• Clustering algorithms such as Fuzzy C-means, ISODATA, and Gaussian Mixture Models (GMM) were tested in combination with variable reduction methods.

Main Results:

• Variable reduction techniques combined with clustering algorithms improved MZ delineation performance, achieving Average Silhouette Coefficients (ASC) of 0.48-0.57 and Variance Reduction (VR) of 13.35-23.13%.
• The MULTISPATI-PCA approach proved more effective than traditional PCA in identifying key variables for MZ delineation.
• The combination of MULTISPATI-PCA and GMM yielded the highest VR and ASC values, indicating superior MZ delineation.

Conclusions:

• Advanced methods integrating spatial analysis and clustering algorithms significantly enhance MZ delineation accuracy.
• MULTISPATI-PCA is a valuable tool for selecting relevant soil variables for MZ delineation.
• The findings support the optimization of MZ delineation for precise nutrient management in modern agriculture.