LiDAR-derived indices and their relationship with productivity and oil quality attributes in high-density olive orchards

Abstract

Optimising the leaf area index (LAI) and light exposure are key aspects to improve yield and quality in high-density olive orchards. Traditionally, these characteristics are estimated using ceptometers, but this method is both labour intensive and dependent on light conditions, with limitations in effectiveness for precision horticulture management purposes. This study investigates the potential of four LiDAR-derived indices to: i) estimate the LAI and canopy light interception under different fertigation treatments, and ii) to assess their impact on yield and oil quality throughout a growing season in a high-density olive orchard. The treatments included two nitrogen dose rates (N0: 0 kg ha-¹ and N50: 50 kg ha-¹) combined with full irrigation (FI) and deficit irrigation (DI). The results provide strong evidence for the effectiveness of these indices in monitoring canopy response to treatments over time. The N50 and FI treatments increased the LAI and yield but led to poorer light attenuation within the canopy and lower oil quality. In contrast, N0 and DI promoted more light-permeable canopies and improved oil quality, with higher phenolic content, better oxidative stability and improved organoleptic oil attributes. These indices can improve the understanding of canopy light environments, help to optimise management practices, and ultimately improve the sustainability and profitability of intensive olive cultivation.