WeChat Mini Program
Old Version Features

Response of Canola and Cereals to Amendment of Subsurface Soil Acidity and a Hardpan

CROP & PASTURE SCIENCE(2023)

New South Wales Dept Primary Ind

Cited 2|Views19
Abstract
Context Limitations to crop yield due to subsurface (5–15 cm depth) compaction layers (>2 MPa) and subsurface acidity (pHCa <4.8) have frequently been reported on the non-sodic soils of south-eastern Australia, but amendment studies have been limited in number and inconsistent in the extent and longevity of any response. Aim We tested the hypothesis that amendment of subsurface acidity and compaction would lead to increased grain yield. Method We investigated crop response to the alleviation of these combined subsurface soil constraints by using deep ripping and dry limestone injection to 30 cm depth over 3 years in a canola–cereal sequence. Key results Deep tillage and injection of limestone into the soil both failed to produce significant grain yield responses in any year, despite the reduction of soil strength and increase in pH in subsurface layers. Early vegetative growth sometimes responded to the treatments, but the loss of stored soil water during drier than average seasons appeared to limit grain response. However, we also observed that a proportion of plant roots penetrated these relatively thin constraint layers in unamended soils. Conclusions Amelioration of subsurface acidity and compaction does not necessarily increase grain yield. Implications The effects of subsurface acidity and compaction should be tested on other species and during varying rainfall deciles. Given the potentially large resource requirements for deep amendment of soils, we propose that the selection of tolerant species and cultivars might be more effective in the short term.
More
Translated text
Key words
Brassica napus,canola,cereals,deep ripping,liming,oilseed rape,rapeseed,subsurface soil constraints
求助PDF
上传PDF
Bibtex
收藏
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