Evidence map›Paper›PMID 39687017›Full record

ArticleiScience2024

Utilizing machine learning to optimize agricultural inputs for improved rice production benefits.

Tao Liu, Xiafei Li, Xinrui Li, Zhonglin Wang, Huilai Yin, Yangming Ma, Yongheng Luo, Ruhongji Liu, Zhixin Li, Pengxin Deng and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Tao LiuCrop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China.
Xiafei LiCrop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China.
Xinrui LiCrop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China.
Zhonglin WangCrop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China.
Huilai YinCrop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China.
Yangming MaCrop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China.
Yongheng LuoCrop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China.
Ruhongji LiuCrop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China.
Zhixin LiCrop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China.
Pengxin DengCrop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China.
Zhenglan PengCrop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China.
Zhiyuan YangCrop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China.
Yongjian SunCrop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China.
Jun MaCrop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China.
Zongkui ChenCrop Ecophysiology and Cultivation Key Laboratory of Sichuan Province, Rice Research Institute / State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lower efficiency of agricultural inputs in the four conventional rice planting methods limits productivity and environmental benefits in Southwest China. Thus, we developed a machine-learning-based decision-making system for achieving optimal comprehensive benefits during rice production. Based on conventional benefits for achieving optimal benefits, implemented strategies in these planting methods: reducing N fertilizer by 16% while increasing seed inputs by 9% in mechanical transplanting (MT) method improved yield and environmental benefits; reducing N fertilizer and seed inputs by 10-12% in mechanical direct seeding (MD) method decreased environmental impacts; increasing N-K fertilizers and seed inputs by 15-33% in manual transplanting (MAT) method improved its comprehensive benefits by 7-14%; applying N-P-K fertilizer ratio of 2:1:2 in manual direct seeding (MAD) method enhanced yield. Our study provides strategies for improving benefits in these planting methods, with MT method being more beneficial for optimizing comprehensive benefits, especially in yield and environmental impacts, in Southwest China.

Indexed as

Agricultural sciencemachine learning

Identifiers

PMID39687017
PMCPMC11648248

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.