Framework development for interpolation optimization in scenarios with different soil sampling densities

• Grant number: 24/01557-3
• Support Opportunities: Scholarships in Brazil - Master
• Start date: June 01, 2024
• End date: March 31, 2026
• Field of knowledge: Agronomical Sciences - Agricultural Engineering
• Principal Investigator: Lucas Rios do Amaral
• Grantee: Laura Delgado Bejarano
• Host Institution: Faculdade de Engenharia Agrícola (FEAGRI). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

Soil is an essential system for life and agriculture; however, it faces degradation issues due to intensive agricultural practices. Precision agriculture seeks to optimize resource management by collecting data on various variables, including soil properties. Nevertheless, spatiotemporal variability poses challenges for precise and efficient decision-making, requiring high-density sampling, which is often costly and labor-intensive. For this reason, this research aims to evaluate the effectiveness of different interpolation techniques in scenarios with varying sample densities in predicting soil fertility attributes. Traditional deterministic, geostatistical, and machine learning methods will be evaluated using data from three areas in the state of São Paulo, Brazil, under different agronomic practices. Spatial characteristics of the data and fertilizer prescription maps will be assessed. The aim is to generate a decision-making framework regarding methods and configurations to be used in each situation. This aims to optimize soil sampling and data analysis for a more profitable and sustainable precision agriculture.