Management zones delineation: a proposal to overcome the crop-pasture rotation challenge

• Published in: Precision agriculture 2025-02, Vol.26 (1), p.21, Article 21
• Main Authors: Oldoni, Henrique, Magalhães, Paulo S. G., Oliveira, Agda L. G., Lima, Joaquim P., Figueiredo, Gleyce K. D. A., Moro, Edemar, Amaral, Lucas R.
• Format: Article
• Language: English
• Subjects: Agricultural production; Agriculture; Atmospheric Sciences; Biomedical and Life Sciences; Chemistry and Earth Sciences; Computer Science; Crop yield; Crops; Delineation; Farming systems; Grain; Life Sciences; Livestock; Pasture; Physics; Remote Sensing/Photogrammetry; Rotation; Soil Science & Conservation; Soybeans; Stability; Statistics for Engineering
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-024-10214-0

Few strategies have been developed to effectively delineate management zones (MZs) in crop-pasture rotation (CPR) systems that accommodate site-specific management for multiple crops using a single map. This study aimed to propose and evaluate several feature selection approaches that account for multiple crops in CPR systems and propose a framework for MZ delineation in CPR systems that results in a single MZ map. The feature selection approaches were based on the spatial correlation between attributes (soil, crops, and terrain attributes) and yield variables (grain and pasture yield, spatial trend of yield, and yield temporal stability). This study was conducted in an area with an integrated crop-livestock system, featuring the CPR of soybean and pasture. The results showed that the approach based on yield temporal stability was the most effective for selecting relevant attributes used in the MZ delineation in CPR systems, resulting in greater differentiation among MZs. A higher number of MZs was needed (four zones), emphasizing the importance of carefully selecting the number based on variance reduction and yield differences to ensure that the final MZ map reflects the variability across all crops and guides their integrated management. The proposed framework is one of the first to use yield temporal stability for feature selection specifically aimed at delineating MZs in CPR systems. This approach improves the ability to select significant attributes used in the MZs delineation process, providing a better solution for improving input use efficiency and maximizing grain and pasture yield in integrated farming systems.