Title

Excel data.CP5.Mixture Experiment

Description

Intercropping has been proposed as a pathway for sustainable agriculture, with the potential to increase yields and product diversity, to stabilize yields, and to reduce fertilizer and pesticide use. Despite these benefits, however, adoption of intercropping is still low. One problem is the selection of suitable crop species and varieties and of appropriate sowing densities in order to find a competitive balance between component crops. Field trials for optimization of these parameters are either restricted to a low number of options to be tested, or create unmanageable amounts of time-consuming work. Technologies of digital agriculture, including the use of drones for obtaining high resolution imagery from the trials and machine learning for image processing may help to expand the number of partner combinations to be tested in field trials. As part of a larger research project (Phenorob), we set up field trials at a low input conventional and an organic site in 2020 with four replicates. Treatments included all possible monocultures and 1:1 intercropping mixture of twelve spring wheat (Triticum aestivum L.) entries (ten cultivars and two 5-component mixtures of these wheat cultivars), and two faba bean (Vicia faba L.) cultivars. All combinations were each sown in two sowing densities. The design resulted in a total number of 320 plots per site. Regular drone flights over both sites were combined with classical agronomic measurements to calibrate information obtained from the imagery. First results showed strong dependence of the mixture effect on the wheat cultivar.

Creator

Madhuri Paul

Publisher

University of Bonn

Publication Year

2021

Resource Type

Dataset

Identifier

7d2188de-a525-4d0c-998c-2a0cfe24ab27