Drone-based assessment of multifunctionality in mixed cropping systems

Abstract

Abstract Modern agriculture faces the dual challenge of sustainably increasing food production while mitigating the environmental impact of intensive monocultures. Mixed cropping, which is the cultivation of multiple species or varieties, may provide ecological benefits that address productivity and environmental sustainability challenges. However, evaluating its multifunctionality in conventional agricultural field experiments is costly and labour-intensive, and small sample sizes and high spatial variability often make it difficult to detect the statistical significance of mixed cropping effects. This study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs) to efficiently assess the multifunctionality of mixed cropping systems. We conducted a field experiment comparing monocultures of oat, rye, and barley; intraspecific mixed cropping combining three oat varieties; and interspecific mixed cropping combining oat, rye, and barley. Using UAV-derived data across the entire field, including vegetation cover, plant height, and the normalised difference vegetation index, we evaluated five multifunctionalities (biomass production, spatial variability in biomass production, early canopy closure, lodging resistance, and lodging resilience). This framework reveals that mixed cropping outperforms monocropping in several key ecological functions. The proposed UAV-based HTP approach enables cost-effective, robust, and scalable evaluation of mixed cropping systems, facilitating their optimisation for multifunctionality and contributing to the advancement of sustainable agriculture.

Plant phenotyping relevance

UAV画像から植被率・草高・NDVIなどの植物形質を取得する高スループット表現型解析フレームワークを導入・検証しており、フェノタイピング手法が研究の中心です。

Code and data availability

The preprint's data availability statement deposits the datasets generated and analysed in the study (UAV-derived phenotypic measurements and field data) on Zenodo with a DOI that appears verbatim in the allowed URL list. No author analysis code or trained models are explicitly deposited.