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INTRODUCTION: Agricultural diversification was highlighted as one of the most promising strategies to achieve sustainable transformation. Diversification of crops in space and time is claimed to enhance ecosystem services and to maintain biodiversity, but also increases labor and time to manage those cropping systems. Therefore, autonomous agricultural robots could play a major role to facilitate the cultivation and management of diversified fields.
OBJECTIVE: In this study, the experimental infrastructure patchCROP, a landscape experiment aiming at the spatial and temporal crop diversification at the landscape scale, was used to test the autonomous, lightweight robot Naio Oz for the mechanical weed control in maize for 0.5 ha fields (Grahmann et al., 2024). Naio Oz was originally developed for vegetable production but can be equipped to control weeds mechanically by harrowing and hoeing in arable crops and thus offers the potential to reduce chemical herbicides. It has an electric engine, works autonomously up to 8 hours and navigates between crop rows through precise RTK GPS signals and previous path planning.
MATERIALS & METHODS: Fields managed with the Naio Oz robot were compared to conventionally managed fields under contrasting soil conditions of silty and sandy sands in the topsoil. The robot was tested in 2022 and 2023 in grain maize, and preliminary data of 2024 will be available for the conference. Weed species and density, vegetative growth and crop yields of grain maize have been determined as well as SOC for each cropping cycle.
RESULTS: In 2022, the mean weed coverage using the robot in silty sand varied between 13% and 35%, whereas the conventional patch had a weed coverage of only 2%. In 2023, mechanical weed control in sandy sand with the robot resulted in 3 to 14% weed coverage, whereas the conventional patch had less than 1% of weeds. The weed coverage in silty sands was lower with mechanical robot weeding, ranging from 1 to 7%, but 2% with chemical weed control. In 2022, maize yields were very low in treatments with mechanical weeding due to prolonged drought conditions over summer, high weed pressure by Chenopodium album and additional water stress with soil movement after harrowing and hoeing. In sandy soils, all treatments experienced yield failure. In 2023, maize yields in silty soils were similar across treatments averaging 9t/ha, but sandy soils resulted in 3 to 5 t/ha. At harvest in 2022, SOC ranged from 1% in silty sand to 0.75% in sandy sands in the first 20 cm soil depth.
DISCUSSION: Due to staff unavailability for robot supervision as well as inefficiencies through missing experience in appropriate seed bed preparation and path planning criteria in 2022, the robot was not used in all reduced patches as previously planned. Hence, the number of interventions varies between fields and years, making a statistical comparison inappropriate. In the second study year, weather conditions hampered proper timing and field entrance of the robot, facing many rainy days and GPS signal interference.
CONCLUSIONS: The study provided in-depth insights for the appropriate and timely use of the robot and showed its potential to support chemical pesticide reduction without yield losses in grain maize when soil water availability is not limited to the crop. Otherwise, we also recognized its limitations in on-farm conditions. With regard to the critical timing of weed management activities, a fleet of robots could increase efficiency, although the technical feasibility for the joint cultivation in the same field still needs to be explored by further development of current robot models.
REFERENCES: Grahmann et al. 2024. Co-designing a landscape experiment to investigate diversified cropping systems. Agricultural Systems (accepted) https://doi.org/10.1016/j.agsy.2024.103950
| Keywords | Digital technologies; Diversified cropping systems; Robotics; Mechanical weeding; Maize |
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