Evolution of Topsoil Structure after Compaction with a Lightweight Autonomous Field Robot
Soil Science Society of America Journal(2024)
Aarhus Univ
Abstract
Soil structure dynamics during a season depend on management practices and environmental factors. A lightweight autonomous robot (total mass: 3300–4100 kg, wheel load: 700–1200 kg, contact areas: 0.125 m2, inflation pressures: 60–280 kPa) was used for sowing (October 2021) and weeding (May 2022) operations on an annually plowed sandy loam field. We took 579 cm3 soil cores at 10‐ to 18‐cm depth in the crop area and wheel tracks before and after the operations to assess the impact from traffic and the potential recovery of topsoil structural properties. We measured air permeability and effective air‐filled porosity in the laboratory, and X‐ray CT scanned the samples to evaluate soil pore functionality. The first operation (conducted on a moist seedbed) had the largest impact, significantly compacting and reducing the air‐filled porosity by 42% (from 0.21 to 0.12 m3 m−3) and decreasing air permeability by 75.8% (from 130 to 31.5 µm2). After 7 months, the crop area and wheel track showed signs of soil consolidation due to environmental factors but not decompaction. The second operation occurred on drier (water content 0.06 g g−1), stronger soil conditions (degree of compactness 100.8%), and recompaction of the wheel track was not observed. Traffic in weak soils can result in seasonal topsoil compaction despite the lighter wheel loads. However, due to the milder impacts, recovery rates might be faster for lightweight machinery than for heavy tractors. Multi‐season studies are needed to assess the real potential of lightweight robots to minimize soil compaction risk.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined