Ridge-furrow Film Mulch with Nitrogen Fertilization Improves Grain Yield of Dryland Maize by Promoting Root Growth, Plant Nitrogen Uptake and Remobilization
Soil and Tillage Research(2024)
Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education
Abstract
Soil mulch has been widely applied to enhance crop yields in (semi-)arid and semi-humid regions on the globe. However, how different mulch practices influence root growth, plant nitrogen (N) uptake, remobilization and allocation remains unclear, especially under different N fertilization rates. A two-season (June–October in 2021 and 2022) field experiment was performed on dryland summer maize in a semi-humid but drought-prone region of northwest China. There were six mulch practices: flat cultivation without mulch (Flat), flat cultivation with straw mulch (Flat-Straw), flat cultivation with black film mulch (Flat-Film), flat cultivation with transparent film mulch (Flat-Trans), ridge-furrow cultivation with black film mulch on the ridge (Ridge-Film), ridge-furrow cultivation with transparent film mulch on the ridge (Ridge-Trans), and two N fertilization rates: 0 kg N ha−1 (N0) and 180 kg N ha−1 (N1). The results showed that film mulch significantly (p<0.05) improved root morphological parameters compared to Flat. Plant N uptake significantly (p<0.05) increased due to improved root growth under film and straw mulch, with grain N uptake increasing by 78.2% under Ridge-Film, 66.2% under Ridge-Trans and 39.6% under Flat-Film compared to Flat. The significant (p<0.01) increases in pre-anthesis N remobilization and post-anthesis N uptake under film and straw mulch compared to Flat achieved synergistic increases in grain yields. The contributions of total N remobilization to grain N dramatically increased by 31.2%, 27.5%, 17.0% and 11.2% under Ridge-Film, Ridge-Trans, Flat-Film and Flat-Trans compared to Flat, respectively. Meanwhile, film-mulched practices significantly (p<0.01) reduced soil nitrate-N residual due to improved plant N uptake, by 21.7% under Flat-Film, 16.9% under Flat-Trans, and 33.6% under Ridge-Film compared to Flat. The contributions of various mulch practices to grain yield ranked as Ridge-Film > Flat-Film > Ridge-Trans > Flat-Trans > Flat-Straw. The Ridge-Film with N fertilization significantly (p<0.05) increased N use efficiency, N agronomic efficiency, N recovery efficiency, N harvest index and grain yield by 10.8%, 124.9%, 147.1%, 19.8% and 120.0% compared to FlatN0, respectively. The structural equation modeling indicated that the differences in post-silking N uptake and pre-silking N remobilization caused by different mulch practices and nitrogen rates could directly explain at least 48.0% and 52.0% of grain yield differences. The ridge-furrow cultivation with film mulch, especially black film mulch, along with 180 kg N ha−1 is favorable for improving grain yield and N use efficiency of dryland summer maize in the semi-humid but drought-prone region of northwest China.
MoreTranslated text
Key words
Grain yield,Nitrogen uptake,Nitrogen remobilization,Nitrogen allocation,Soil nitrogen residual
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
Related Papers
JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024
被引用0
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