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This study focuses on optimizing soil sampling designs in precision agriculture by defining application zones (AZs) based on soil chemical properties. Using five clustering methods, particularly K-means and Ward, the research successfully reduced sample sizes while maintaining accuracy in soil property assessments. The findings suggest that optimized sampling can enhance decision-making in agricultural practices and lower laboratory analysis costs.

Author

Alexander Rodriguez

Language

EN